Session-based recommendation (SBR) in service computing is pivotal in predicting a user's next action based on their current anonymous session. While Graph Neural Network (GNN)-based methods have shown promise in capturing intricate item transformation relationships within sessions, they often fall short in accurately modeling user preferences. This is primarily due to the common practice of solely considering the last item in the session as the user's current interest, neglecting potentially valuable information embedded in other session items which is essential for capturing user global preferences. Moreover, existing models typically optimize performance solely through cross-entropy loss between predicted items and ground truth labels, while overlooking latent valuable knowledge embedded in intermediate features and item-item relationships that lends support to the model in accurately capturing and modeling user preferences. To address these shortcomings, we propose Multi-Scale Collaborative Distillation (MSCD) for SBR. Our approach introduces a current interest adaptive selection module, which dynamically selects appropriate item embeddings as session-local embeddings by evaluating the importance of each item within the session. This allows for a more accurate capture of the user's current true preferences. Additionally, we propose collaborative knowledge distillation, where multiple models are trained concurrently, enabling the transfer of three types of knowledge including response-based, feature-based, and relationship-based knowledge between models, thereby enriching the model's understanding of user preferences. Experimental evaluations conducted on three popular SBR datasets demonstrate that our MSCD model outperforms recent state-of-the-art methods in terms of recommendation accuracy. Our codes are available at:https://github.com/lonely-ice/MSCD.

Sequential recommendation seeks to understand user preferences based on their past actions and predict future interactions with items. Recently, several techniques for sequential recommendation have emerged, primarily leveraging graph convolutional networks (GCNs) for their ability to model relationships effectively. However, real-world scenarios often involve sparse interactions, where early and recent short-term preferences play distinct roles in the recommendation process. Consequently, vanilla GCNs struggle to effectively capture the explicit correlations between these early and recent short-term preferences. To address these challenges, we introduce a novel approach termed Graph Convolutional Networks with Collaborative Feature Fusion (COFF). Specifically, our method addresses the issue by initially dividing each user interaction sequence into two segments. We then construct two separate graphs for these segments, aiming to capture the user's early and recent short-term preferences independently. To obtain robust prediction, we employ multiple GCNs in a collaborative distillation manner, incorporating a feature fusion module to establish connections between the early and recent short-term preferences. This approach enables a more precise representation of user preferences. Experimental evaluations conducted on five popular sequential recommendation datasets demonstrate that our COFF model outperforms recent state-of-the-art methods in terms of recommendation accuracy.

With the development of medical technology, ultrasonography has become an important diagnostic method in doctors' clinical work. However, compared with the static medical image processing work such as CT, MRI, etc., which has more research bases, ultrasonography is a dynamic medical image similar to video, which is captured and generated by a real-time moving probe, so how to deal with the video data in the medical field and cross modal extraction of the textual semantics in the medical video is a difficult problem that needs to be researched. For this reason, this paper proposes a pre-training model of multimodal distillation and fusion coding for processing the semantic relationship between ultrasound dynamic Images and text. Firstly, by designing the fusion encoder, the visual geometric features of tissues and organs in ultrasound dynamic images, the overall visual appearance descriptive features and the named entity linguistic features are fused to form a unified visual-linguistic feature, so that the model obtains richer visual, linguistic cues aggregation and alignment ability. Then, the pre-training model is augmented by multimodal knowledge distillation to improve the learning ability of the model. The final experimental results on multiple datasets show that the multimodal distillation pre-training model generally improves the fusion ability of various types of features in ultrasound dynamic images, and realizes the automated and accurate annotation of ultrasound dynamic images.

The previous relation-based knowledge distillation methods tend to construct global similarity relationship matrix in a mini-batch while ignoring the knowledge of neighbourhood relationship. In this paper, we propose a new similarity-based relational knowledge distillation method that transfers neighbourhood relationship knowledge by selecting K-nearest neighbours for each sample. Our method consists of two components: Neighbourhood Feature Relationship Distillation and Neighbourhood Logits Relationship Distillation. We perform extensive experiments on CIFAR100 and Tiny ImageNet classification datasets and show that our method outperforms the state-of-the-art knowledge distillation methods. Our code is available at: https://github.com/xinxiaoxiaomeng/NRKD.git.

Knowledge distillation (KD) is a prevalent model compression technique in deep learning, aiming to leverage knowledge from a large teacher model to enhance the training of a smaller student model. It has found success in deploying compact deep models in intelligent applications like intelligent transportation, smart health, and distributed intelligence. Current knowledge distillation methods primarily fall into two categories: offline and online knowledge distillation. Offline methods involve a one-way distillation process, transferring unvaried knowledge from teacher to student, while online methods enable the simultaneous training of multiple peer students. However, existing knowledge distillation methods often face challenges where the student may not fully comprehend the teacher's knowledge due to model capacity gaps, and there might be knowledge incongruence among outputs of multiple students without teacher guidance. To address these issues, we propose a novel reciprocal teacher-student learning inspired by human teaching and examining through forward and feedback knowledge distillation (FFKD). Forward knowledge distillation operates offline, while feedback knowledge distillation follows an online scheme. The rationale is that feedback knowledge distillation enables the pre-trained teacher model to receive feedback from students, allowing the teacher to refine its teaching strategies accordingly. To achieve this, we introduce a new weighting constraint to gauge the extent of students' understanding of the teacher's knowledge, which is then utilized to enhance teaching strategies. Experimental results on five visual recognition datasets demonstrate that the proposed FFKD outperforms current state-of-the-art knowledge distillation methods.

Deep dictionary learning (DDL) shows good performance in visual classification tasks. However, almost all existing DDL methods ignore the locality relationships between the input data representations and the learned dictionary atoms, and learn sub-optimal representations in the feature coding stage, which are less conducive to classification. To this end, we propose a hierarchical locality-aware deep dictionary learning (HILADLE) framework for classification, which can learn locality-constrained dictionaries at different abstract levels through hierarchical dictionary learning. The locality constraints play an important role in learning informative dictionary atoms while preserving the data structure in the original input feature space. Moreover, instead of using an identity activation function like existing DDL methods, we further boost the generalization performance of our HILADLE method with a ReLU activation function to deal with the overfitting issue caused by over-parameterization, inspired by its effectiveness in deep neural networks. Finally, the concatenation of all feature representations learned at different layers is used as input to the final classifier. We demonstrate, through an extensive set of experiments on several benchmark face recognition, image classification, and age estimation datasets, that our method is able to surpass several dictionary learning, deep dictionary learning and deep learning methods.

The application of Auto-Encoder (AE) to multi-view representation learning has gained traction due to advancements in deep learning. While some current AE-based multi-view representation learning algorithms incorporate the geometric structure of the input data into their feature representation learning process, their use of a shallow structured graph regularization term can be restrictive when used in conjunction with deep models. Furthermore, current multi-view representation learning algorithms do not fully utilize the diversity and consistency presented in different views, leading to a reduction in the efficacy of feature learning. This paper introduces a novel approach, reconstructed graph constrained auto-encoders (RGCAE), for multi-view representation learning. Unlike existing methods, our approach incorporates deep adaptive graph regularization based on multi-layer perceptron to ensure the preservation of the geometric similarity graph, which is constructed based on the local invariance principle. By decoupling the feature representation learning from the preservation of the geometric structure among different views, our approach can better leverage the diversity presented in multi-view data. We obtain view-specific representations that preserve the geometric structure and then combine them by averaging to obtain a common representation. To ensure the consistency of the multi-view data, we minimize the loss between the view-specific and common representations. Consequently, our RGCAE approach can maintain the geometric structure of multi-view data and is better suited for integration with deep models. Extensive experiments on six datasets demonstrate that RGCAE obtained promising performance, compared with the state-of-the-art methods.

In recent years, various distillation methods for semantic segmentation have been proposed. However, these methods typically train the student model to imitate the intermediate features or logits of the teacher model directly, thereby overlooking the high-discrepancy regions learned by both models, particularly the differences in instance edges. In this paper, we introduce a novel approach, called Difference-aware Distillation, to address this limitation. Our proposed method detects the discrepancies among the teacher model and the student model in the logit space through two masking mechanisms (i.e., masking by logit differences with respect to the ground truth labels and masking by differences in the predictive class probabilities), and guides the student model to restore the teacher's features with the focus on these highly-discrepant regions, resulting in improved segmentation performance. With the features jointly masked by these two mechanisms, the student model learns to preserve the teacher's features via a feature generation module, thus achieving better representation. Our experimental evaluation on three datasets, Cityscapes, Pascal2012, and ADE20 K, demonstrates our proposed approach outperforms several baselines considered. Further visualization analysis confirms that our method effectively directs the student model's attention to the discrepancies, such as the edges of small objects and the interiors of large objects.

Knowledge distillation (KD), as an efficient and effective model compression technique, has received considerable attention in deep learning. The key to its success is about transferring knowledge from a large teacher network to a small student network. However, most existing KD methods consider only one type of knowledge learned from either instance features or relations via a specific distillation strategy, failing to explore the idea of transferring different types of knowledge with different distillation strategies. Moreover, the widely used offline distillation also suffers from a limited learning capacity due to the fixed large-to-small teacher-student architecture. In this article, we devise a collaborative KD via multiknowledge transfer (CKD-MKT) that prompts both self-learning and collaborative learning in a unified framework. Specifically, CKD-MKT utilizes a multiple knowledge transfer framework that assembles self and online distillation strategies to effectively: 1) fuse different kinds of knowledge, which allows multiple students to learn knowledge from both individual instances and instance relations, and 2) guide each other by learning from themselves using collaborative and self-learning. Experiments and ablation studies on six image datasets demonstrate that the proposed CKD-MKT significantly outperforms recent state-of-the-art methods for KD.

Knowledge distillation (KD) is a powerful and widely applicable technique for the compression of deep learning models. The main idea of knowledge distillation is to transfer knowledge from a large teacher model to a small student model, where the attention mechanism has been intensively explored in regard to its great flexibility for managing different teacher-student architectures. However, existing attention-based methods usually transfer similar attention knowledge from the intermediate layers of deep neural networks, leaving the hierarchical structure of deep representation learning poorly investigated for knowledge distillation. In this paper, we propose a hierarchical multi-attention transfer framework (HMAT) , where different types of attention are utilized to transfer the knowledge at different levels of deep representation learning for knowledge distillation. Specifically, position-based and channel-based attention knowledge characterize the knowledge from low-level and high-level feature representations, respectively, and activation-based attention knowledge characterize the knowledge from both mid-level and high-level feature representations. Extensive experiments on three popular visual recognition tasks, image classification, image retrieval, and object detection, demonstrate that the proposed hierarchical multi-attention transfer or HMAT significantly outperforms recent state-of-the-art KD methods.

Abstract Point cloud completion is prevalent due to the insufficient results from current point cloud acquisition equipments, where a large number of point data failed to represent a relatively complete shape. Existing point cloud completion algorithms, mostly encoder‐decoder structures with grids transform (also presented as folding operation), can hardly obtain a persuasive representation of input clouds due to the issue that their bottleneck‐shape result cannot tell a precise relationship between the global and local structures. For this reason, this article proposes a novel point cloud completion model based on a Stack‐Style Folding Network (SSFN). Firstly, to enhance the deep latent feature extraction, SSFN enhances the exploitation of shape feature extractor by integrating both low‐level point feature and high‐level graphical feature. Next, a precise presentation is obtained from a high dimensional semantic space to improve the reconstruction ability. Finally, a refining module is designed to make a more evenly distributed result. Experimental results shows that our SSFN produces the most promising results of multiple representative metrics with a smaller scale parameters than current models.

Abstract Knowledge distillation is a simple yet effective technique for deep model compression, which aims to transfer the knowledge learned by a large teacher model to a small student model. To mimic how the teacher teaches the student, existing knowledge distillation methods mainly adapt an unidirectional knowledge transfer, where the knowledge extracted from different intermedicate layers of the teacher model is used to guide the student model. However, it turns out that the students can learn more effectively through multi-stage learning with a self-reflection in the real-world education scenario, which is nevertheless ignored by current knowledge distillation methods. Inspired by this, we devise a new knowledge distillation framework entitled multi-target knowledge distillation via student self-reflection or MTKD-SSR, which can not only enhance the teacher’s ability in unfolding the knowledge to be distilled, but also improve the student’s capacity of digesting the knowledge. Specifically, the proposed framework consists of three target knowledge distillation mechanisms: a stage-wise channel distillation (SCD), a stage-wise response distillation (SRD), and a cross-stage review distillation (CRD), where SCD and SRD transfer feature-based knowledge (i.e., channel features) and response-based knowledge (i.e., logits) at different stages, respectively; and CRD encourages the student model to conduct self-reflective learning after each stage by a self-distillation of the response-based knowledge. Experimental results on five popular visual recognition datasets, CIFAR-100, Market-1501, CUB200-2011, ImageNet, and Pascal VOC, demonstrate that the proposed framework significantly outperforms recent state-of-the-art knowledge distillation methods.

Hyperspectral image (HSI) denoising is a challenging task, not only because it is unavoidably contaminated by severe mixed noises, but also for its hard-to-recover spatial-spectral structure. Since it has been found that HSI has low-rank property, low-rank models have received extensive attention in dealing with the HSI denoising task. However, these models either use nuclear norm, which can only obtain sub-optimal solutions, or require some predefined information that is difficult to determine. To address these issues, in this paper we propose a new HSI denoising model based on non-convex fraction function, which has excellent performance in removing mixed noises. Specifically, the proposed model can capture the rank information of HSI automatically, which allows it to separate clean HSI from noises more accurately. Then, an iterative optimization algorithm is developed by exploiting the framework of the augmented Lagrange multiplier (ALM). Meanwhile, the subproblems at each iteration can be solved by the proximal operator with a closed-form solution. Besides, the convergence of the proposed algorithm is also provided theoretically. Extensive experiments implemented with simulated and real datasets demonstrate that our proposed model performs better than state-of-the-art models in HSI denoising. MATLAB code is available at https://github.com/wangzhi-swu/HSI-Denosing.

Recently, model compression has been widely used for the deployment of cumbersome deep models on resource-limited edge devices in the performance-demanding industrial Internet of Things (IoT) scenarios. As a simple yet effective model compression technique, knowledge distillation (KD) aims to transfer the knowledge (e.g., sample relationships as the relational knowledge) from a large teacher model to a small student model. However, existing relational KD methods usually build sample correlations directly from the feature maps at a certain middle layer in deep neural networks, which tends to overfit the feature maps of the teacher model and fails to address the most important sample regions. Inspired by this, we argue that the characteristics of important regions are of great importance, and thus, introduce attention maps to construct sample correlations for knowledge distillation. Specifically, with attention maps from multiple middle layers, attention-based sample correlations are newly built upon the most informative sample regions, and can be used as an effective and novel relational knowledge for knowledge distillation. We refer to the proposed method as multilevel attention-based sample correlations for knowledge distillation (or MASCKD). We perform extensive experiments on popular KD datasets for image classification, image retrieval, and person reidentification, where the experimental results demonstrate the effectiveness of the proposed method for relational KD.

Cross-modal hashing is an effective cross-modal retrieval approach because of its low storage and high efficiency. However, most existing methods mainly utilize pre-trained networks to extract modality-specific features, while ignore the position information and lack information interaction between different modalities. To address those problems, in this paper, we propose a novel approach, named cross-modal generation and pair correlation alignment hashing (CMGCAH), which introduces transformer to exploit position information and utilizes cross-modal generative adversarial networks (GAN) to boost cross-modal information interaction. Concretely, a cross-modal interaction network based on conditional generative adversarial network and pair correlation alignment networks are proposed to generate cross-modal common representations. On the other hand, a transformer-based feature extraction network (TFEN) is designed to exploit position information, which can be propagated to text modality and enforce the common representation to be semantically consistent. Experiments are performed on widely used datasets with text-image modalities, and results show that the proposed method achieved competitive performance compared with many existing methods.

Deep dictionary learning (DDL) aims to learn dictionaries at different levels and the deepest level representations. However, existing DDL algorithms impose a$l_{1}$-norm constraint on the deepest level representations, ignoring the constraints on different level representations. Meanwhile, they fail to discover effectively the essential discrimination information. Therefore, the obtained representations are less discriminative, which degrades model performance. To tackle those issues, we propose an intra- and inter-class induced discriminative deep dictionary learning (DDDL). Specifically, both intra-class compactness and inter-class separability of layer-wise data representations are newly devised as two discriminative constraints on deep dictionary learning. In a hierarchical structure, we obtain a more informative dictionary and the class-specific representations are thus more discriminative at each layer. Due to the$l_{2}$-norm intra- and inter-class constraints of layer-wise data representation, we devise a layer-wise optimization strategy to efficiently learn the closed-form solution of the deepest representation for classification. Comprehensive experiments and analyses on several visual recognition tasks show that our DDDL model surpasses recent shallow and deep representation learning approaches.

Canonical correlation analysis (CCA) matters in multi-view representation learning. But, CCA and its most variants are essentially based on explicit or implicit covariance matrices. It means that they have no ability to model the nonlinear relationship among features due to intrinsic linearity of covariance. In this paper, we address the preceding problem and propose a novel canonical F-correlation framework by exploring and exploiting the nonlinear relationship between different features. The framework projects each feature rather than observation into a certain new space by an arbitrary nonlinear mapping, thus resulting in more flexibility in real applications. With this frame-work as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping. Moreover, we further propose a multiset version of CCP dubbed MCCP for learning compact representation of more than two views. The proposed MCCP is solved by an iterative method, and we prove the convergence of this iteration. A series of experimental results on six benchmark datasets demonstrate the effectiveness of our proposed CCP and MCCP methods.

In recent years, deep dictionary learning (DDL)has attracted a great amount of attention due to its effectiveness for represen-tation learning and visual recognition. However, most existing methods focus on unsupervised deep dictionary learning, failing to further explore the category information. To make full use of the category information of different samples, we pro-pose a novel deep dictionary learning model with an intra-class constraint (DDLIC) for visual classification. Specif-ically, we design the intra-class compactness constraint on the intermediate representation at different levels to encour-age the intra-class representations to be closer to each other, and eventually the learned representation becomes more dis-criminative. Unlike the traditional DDL methods, during the classification stage, our DDLIC performs a layer-wise greedy optimization in a similar way to the training stage. Experi-mental results on four image datasets show that our method is superior to the state-of-the-art methods.

Image classification is a fundamental component in modern computer vision systems, where sparse representation-based classification has drawn a lot of attention due to its robustness. However, on the optimization of sparse learning systems, regularization and data augmentation are both powerful, but currently isolated. We believe that regularization and data augmentation can cooperate to generate a breakthrough in robust image classification. In this article, we propose a novel framework, regularization on augmented data (READ), which creates diversification in the data using the generic augmentation techniques to implement robust sparse representation-based image classification. When the training data are augmented, READ applies a distinct regularizer,$l_{1}$or$l_{2}$, in particular, on the augmented training data apart from the original data, so that regularization and data augmentation are utilized and enhanced synchronously. We introduce an elaborate theoretical analysis on how to optimize the sparse representation by both$l_{1}$-norm and$l_{2}$-norm with the generic data augmentation and demonstrate its performance in extensive experiments. The results obtained on several facial and object datasets show that READ outperforms many state-of-the-art methods when using deep features.

Recently, deep dictionary learning (DDL) has aroused attention due to its abilities of learning multiple different dictionaries and extracting multi-level abstract feature representations for samples. It has been applied to many intelligent recognition tasks, such as vehicle detection, traffic sign recognition and driver monitoring. Nevertheless, the off-the-shelf DDL-based methods ignore the essential structural information of data in multi-layer dictionary learning. The learned hierarchical data representations are less discriminative. To address this issue, we develop a new DDL framework, called the hierarchical graph augmented deep collaborative dictionary learning (HGDCDL). Firstly, we propose a new deep collaborative dictionary learning (DCDL) that applies collaborative representation to the deepest-level representation learning. Most importantly, equipped with a simple yet effective hierarchal graph construction mechanism, our HGDCDL uses the structure of data to regularize dictionary learning, and generates more informative dictionaries and discriminative representations at different levels. Extensive experiments show that our HGDCDL performs significantly better than the state-of-the-art shallow and deep representation learning methods for classification.

Summary In this paper, we propose a composite nonlinear multiset canonical correlation projections (CNMCPs) framework where orthogonal constraints are imposed in each set. This makes CNMCP capable of learning uncorrelated low‐dimensional features with minimum redundancy in Hilbert space. With the CNMCP framework, we further present a particular algorithm called multikernel multiset canonical correlations or mKMCC, which introduces different weights into multiple nonlinear functions in all views. An alternating iterative optimization is designed for computational solution. Numerous experimental results on practical datasets have demonstrated the effectiveness and robustness of mKMCC, in contrast with existing kernel correlation learning approaches.

Representation-based classification (RBC) has been attracting a great deal of attention in pattern recognition. As a typical extension to RBC, collaborative representation-based classification (CRC) has demonstrated its superior performance in various image classification tasks. Ideally, we expect that the learned class-specific representations for a testing sample are discriminative, and the representation computed for the true class dominates the final representation of the testing sample. Most existing CRC-based methods can learn pattern discrimination, but cannot differentiate the contribution of class-specific representations to the classification of each testing sample. It is challenging for a representation-based classifier to retain both properties. To address this challenge and further improve CRC's classification performance, we propose a novel CRC-based method, class mean-weighted discriminative collaborative representation-based classifier (CMW-DCRC). Its objective function penalises the standard l 2 -norm residuals with two discriminative regularisation terms. A decorrelating term makes the class-specific representations more discriminative, and a newly designed class mean-weighted term that promotes the training samples from individual classes to competitively reconstruct the testing sample while boosting the contribution of the true class. To further enhance the robustness of CRC, we extend CMW-DCRC by replacing the l2-norm coding residual with a l1-norm coding residual, and solve the optimisation problem with an iteratively reweighted least square algorithm. Extensive experimental results on nine image data sets have shown that our methods outperform the state-of-the-art RBC-based methods.

Component analysis (CA) is a powerful technique for learning discriminative representations in various computer vision tasks. Typical CA methods are essentially based on the covariance matrix of training data. But, the covariance matrix has obvious disadvantages such as failing to model complex relationship among features and singularity in small sample size cases. In this letter, we propose a general covariance measure to achieve better data representations. The proposed covariance is characterized by a nonlinear mapping determined by domain-specific applications, thus leading to more advantages, flexibility, and applicability in practice. With general covariance, we further present two novel CA methods for learning compact representations and discuss their differences from conventional methods. A series of experimental results on nine benchmark data sets demonstrate the effectiveness of the proposed methods in terms of accuracy.

Dictionary-based classification has been promising in knowledge discovery from image data, due to its good performance and interpretable theoretical system. Dictionary learning effectively supports both small- and large-scale datasets, while its robustness and performance depends on the atoms of the dictionary most of the time. Empirically, using a large number of atoms is helpful to obtain a robust classification, while robustness cannot be ensured when setting a small number of atoms. However, learning a huge dictionary dramatically slows down the speed of classification, which is especially worse on the large-scale datasets. To address the problem, we propose a Fast and Robust Dictionary-based Classification (FRDC) framework, which fully utilizes the learned dictionary for classification by staging - and -norms to obtain a robust sparse representation. The new objective function, on the one hand, introduces an additional -norm term upon the conventional -norm optimization, which generates a more robust classification. On the other hand, the optimization based on both - and -norms is solved in two stages, which is much easier and faster than current solutions. In this way, even when using a limited size of dictionary, which makes sure the classification runs very fast, it still can gain higher robustness for multiple types of image data. The optimization is then theoretically analyzed in a new formulation, close but distinct to elastic-net, to prove it is crucial to improve the performance under the premise of robustness. According to our extensive experiments conducted on four image datasets for face and object classification, FRDC keeps generating a robust classification no matter whether using a small or large number of atoms. This guarantees a fast and robust dictionary-based image classification. Furthermore, when simply using deep features extracted via some popular pre-trained neural networks, it outperforms many state-of-the-art methods on the specific datasets.

Face hallucination (FH) is an effective technique for super-resolving low-resolution (LR) face images. In real-world applications, a face image usually has multiple distinct low resolutions. Most existing FH methods can not effectively deal with multiple LR views simultaneously. To solve this issue, we present a multi-set partial least squares (MPLS) approach and its kernel extension for jointly learning the nonlinear consistency of multi-resolution facial features. With nonlinear MPLS, we present a novel simultaneous super-resolution coherent facial feature method for the face images with multiple LRs, which has capacity of jointly learning the nonlinear relationships between multiple facial resolutions. Experimental results demonstrate the effectiveness and robustness of our proposed FH method.

In pattern recognition, the representation-based classification (RBC) has attracted much attention recently. As a representative one of RBC, collaborative representation-based classification (CRC) and its variants have achieved promising classification performance in many visual classification tasks. However, most of the CRC methods cannot directly consider the class discrimination information of data that is very important for classification. To fully use the class discrimination information, we propose a novel discriminative group collaborative competitive representation-based classification method (DGCCR) in this paper. In the designed DGCCR model, the discriminative competitive relationships of classes, the discriminative decorrelations among classes and the weighted class-specific group constraints are simultaneously taken into account for strengthening the power of pattern discrimination. Experiments on three visual classification data sets demonstrate that the proposed DGCCR out-performs state-of-the-art RBC methods.

Face super-resolution (FSR) is an effective way to solve low-resolution (LR) problems in face analysis. But, most FSR methods only consider that LR face images have a single resolution, which is usually not consistent with practical situations due to the existence of multiple resolutions. To date, simultaneously learning the mappings from multiple LRs to high resolution (HR) has not been given proper attention. To solve this issue, we first propose a multi-set partial least squares (MPLS) approach to jointly deal with multi-set random variables via a recursive optimization. With MPLS, we then present a novel FSR method called MPLS-FH to simultaneously learn multiple resolution-specific mappings for various LR views from the same source. Concretely, MPLS-FH first divides multi-resolution face images into many patches. Then, it jointly learns the latent coherent features of principal-component embeddings of multi-resolution patches. Last, it super-resolves the input LR face by cross-resolution neighborhood search. Experimental results demonstrate the effectiveness of the proposed method in terms of quantitative and qualitative evaluations.

This study proposes a novel dimensionality reduction (DR) method for multi‐view datasets. The principal component analysis (PCA) idea of minimising least squares reconstruction errors is extended to consider both data distribution and penalty weights called dictionary to recover outliers free global structures from missing and noisy data points. In this way, PCA is viewed as a special instance of the authors’ proposed dictionary induced least squares framework (DLS). Furthermore, to appropriately handle multi‐view DR, we combine the DLS with multiple manifold embeddings (DLSME). Therefore it can obtain lower projections while maintaining a balance between preserving global structures with DLS and local structures with multi‐manifold embeddings. Extensive experiments on object and face recognition datasets verify that the DLS achieves better classification results with lower dimensional projections than PCA. Also, on many multi‐view datasets of visual recognition and web image annotation, the DLSME method demonstrates more effectiveness than Graph‐Laplacian PCA (gLPCA), robust PCA‐optimal mean, canonical correlation analysis (CCA), bilinear models (BLM), neighbourhood preserving embedding, locality preserving projections, and locality sensitive discriminant analysis.

K -nearest neighbor classification method (KNN), as one of the top 10 algorithms in data mining, is a very simple and yet effective nonparametric technique for pattern recognition. However, due to the selective sensitiveness of the neighborhood size k , the simple majority vote, and the conventional metric measure, the KNN-based classification performance can be easily degraded, especially in the small training sample size cases. In this article, to further improve the classification performance and overcome the main issues in the KNN-based classification, we propose a local mean representation-based k -nearest neighbor classifier (LMRKNN). In the LMRKNN, the categorical k -nearest neighbors of a query sample are first chosen to calculate the corresponding categorical k -local mean vectors, and then the query sample is represented by the linear combination of the categorical k -local mean vectors; finally, the class-specific representation-based distances between the query sample and the categorical k -local mean vectors are adopted to determine the class of the query sample. Extensive experiments on many UCI and KEEL datasets and three popular face databases are carried out by comparing LMRKNN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed LMRKNN outperforms the related competitive KNN-based methods with more robustness and effectiveness.

Deep convolutional neural networks provide a powerful feature learning capability for image classification. The deep image features can be utilized to deal with many image understanding tasks like image classification and object recognition. However, the robustness obtained in one dataset can be hardly reproduced in the other domain, which leads to inefficient models far from state-of-the-art. We propose a deep collaborative weight-based classification (DeepCWC) method to resolve this problem, by providing a novel option to fully take advantage of deep features in classic machine learning. It firstly performs the $l_2$-norm based collaborative representation on the original images, as well as the deep features extracted by deep CNN models. Then, two distance vectors, obtained based on the pair of linear representations, are fused together via a novel collaborative weight. This collaborative weight enables deep and classic representations to weigh each other. We observed the complementarity between two representations in a series of experiments on 10 facial and object datasets. The proposed DeepCWC produces very promising classification results, and outperforms many other benchmark methods, especially the ones claimed for Fashion-MNIST. The code is going to be published in our public repository\footnote{https://github.com/zengsn/research}.

Linear reconstruction measure (LRM) is a promising similarity measure of data. In this paper, we consider the locality of data in LRM, and propose weighted two-phase linear reconstruction measure-based classification (WTPLRMC). In WTPLRMC, the first phase determines the representative training samples from all training samples by LRM, and the second phase constrains the linear reconstruction coefficients of the chosen representative training samples in first phase using the locality of data, which is reflected by the similarity weights between each test sample and the representative training samples. The effectiveness of the proposed WTPLRMC is well demonstrated on some benchmark face databases with satisfactory classification results.

In this paper, we propose a multi-local means based nearest neighbor classifier (MLMNN). In the MLMNN, k categorical nearest neighbors of a query sample are first found and used to calculate the corresponding k categorical multi-local mean vectors which can represent different local class-specific sample distributions. Then, the query sample is represented by a linear combination of k categorical local mean vectors and the representation coefficient of each local mean vector as the contribution to representing and classifying the query sample is obtained. Finally, the class-specific representation-based distance (i.e. reconstruction residual) between the query sample and k categorical multi-local mean vectors is adopted to determine the class label of the query sample. The experimental results on three popular face databases show that the proposed MLMNN method outperforms the related competitive KNN-based methods.

In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving graph embedding (DSPGE). Unlike many existing graph embedding methods such as locality preserving projections (LPP) and sparsity preserving projections (SPP), the aim of DSPGE is to preserve the sparse reconstructive relationships of data while simultaneously capture the geometric and discriminant structure of data in the embedding space. Through the sparse reconstruction and class-specific adjacent graphs, DSPGE characterizes the intra-class and inter-class sparsity preserving scatters, seeking to achieve the optimal projections that simultaneously maximize the inter-class sparsity preserving scatter and minimize intra-class sparsity preserving scatter. The effectiveness of the proposed DSPGE is demonstrated on two popular face databases, compared to up-to-date methods. The experimental results show that DSPGE outperforms the competing methods with the satisfactory classification performance.

In this article, we develop a linear supervised subspace learning method called locality-based discriminant neighborhood embedding (LDNE), which can take advantage of the underlying submanifold-based structures of the data for classification. Our LDNE method can simultaneously consider both ‘locality ’ of locality preserving projection (LPP) and ‘discrimination ’ of discriminant neighborhood embedding (DNE) in manifold learning. It can find an embedding that not only preserves local information to explore the intrinsic submanifold structure of data from the same class, but also enhances the discrimination among submanifolds from different classes. To investigate the performance of LDNE, we compare it with the state-of-the-art dimensionality reduction techniques such as LPP and DNE on publicly available datasets. Experimental results show that our LDNE can be an effective and robust method for classification.

K-nearest neighbor (KNN) rule is a simple and effective algorithm in pattern classification. In this article, we propose a local mean-based k-nearest centroid neighbor classifier that assigns to each query pattern a class label with nearest local centroid mean vector so as to improve the classification performance. The proposed scheme not only takes into account the proximity and spatial distribution of k neighbors, but also utilizes the local mean vector of k neighbors from each class in making classification decision. In the proposed classifier, a local mean vector of k nearest centroid neighbors from each class for a query pattern is well positioned to sufficiently capture the class distribution information. In order to investigate the classification behavior of the proposed classifier, we conduct extensive experiments on the real and synthetic data sets in terms of the classification error. Experimental results demonstrate that our proposed method performs significantly well, particularly in the small sample size cases, compared with the state-of-the-art KNN-based algorithms.