Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a multimodal learning pipeline to predict PAWP marker. We utilize complementary information from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal features from CMR scans using tensor-based learning. We propose a graph attention network to select important EHR features for prediction, where we model subjects as graph nodes and feature relationships as graph edges using the attention mechanism. We design four feature fusion strategies: early, intermediate, late, and hybrid fusion. With a linear classifier and linear fusion strategies, our pipeline is interpretable. We validate our pipeline on a large dataset of ${2},{641}$ subjects from our ASPIRE registry. The comparative study against state-of-the-art methods confirms the superiority of our pipeline. The decision curve analysis further validates that our pipeline can be applied to screen a large population. The code is available at https://github.com/prasunc/hemodynamics.

Every ReLU Neural Network (NN) tessellates its input space into activation regions. Studying this tessellation provides insights into some of the architecture’s properties. Recent research has focused on computing the tessellation generated by the output neurons, with the main objective of counting the number of generated regions. This tessellation is achieved through the encoding of each activation region using its bounding hyperplanes. In contrast, we introduce SkelEx, a novel variation of this extraction technique that encodes the extracted regions using their vertices. Next, we introduce BoundEx, which is the first algorithm designed to transform the tessellations of the output neurons into the learned decision boundary defined via the membership polytopes. We highlight the geometric perspective on forward propagation and inference introduced by SkelEx and BoundEx, allowing for more interpretable and intuitive insights. We do so by providing: 1) explanations to the impact of earlier layers; and 2) new perspective on the existence of adversarial examples together with their categorization. The code is available on https://github.com/PawPuk/SkelEx-BoundEx.

Incorporating knowledge graphs (KGs) into recommender systems (RS) has recently attracted increasing attention. For large-scale KGs, due to limited labour supervision, noises are inevitably introduced during automatic construction. However, the effects of such noises as untrustworthy information in KGs on RS are unclear, and how to retain RS performing well while encountering such untrustworthy information has yet to be solved. Motivated by them, we study the effects of the trustworthiness of the KG on RS and propose a novel method trustworthiness-aware knowledge graph representation (KGR) for recommendation (TrustRec). TrustRec introduces a trustworthiness estimator into noise-tolerant KGR methods for collaborative filtering. Specifically, to assign trustworthiness, we leverage internal structures of KGs from microscopic to macroscopic levels: motifs, communities and global information, to reflect the true degree of triple expression. Building on this estimator, we then propose trustworthiness integration to learn noise-tolerant KGR and item representations for RS. We conduct extensive experiments to show the superior performance of TrustRec over state-of-the-art recommendation methods.

Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks. Existing popular methods either capture semantic relationships but indirectly leverage node/edge attributes in a complex way, or leverage node/edge attributes directly without taking semantic relationships into account. When involving multiple convolution operations, they also have poor scalability. To overcome these limitations, this paper proposes a flexible and efficient Gr aph i nformation p ropagation Net work (GripNet) framework. Specifically, we introduce a new supergraph data structure consisting of supervertices and superedges. A supervertex is a semantically-coherent subgraph. A superedge defines an information propagation path between two supervertices. GripNet learns new representations for the supervertex of interest by propagating information along the defined path using multiple layers. We construct multiple large-scale graphs and evaluate GripNet against competing methods to show its superiority in link prediction, node classification, and data integration. The code and data are available at https://github.com/nyxflower/GripNet .

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person video action recognition is an under-explored problem, with a lack of benchmark datasets and limited consideration of first-person video characteristics. Existing benchmark datasets provide videos with a single activity scene, e.g. kitchen, and similar global video statistics. However, multiple activity scenes and different global video statistics are still essential for developing robust UDA networks for real-world applications. To this end, we first introduce two first-person video domain adaptation datasets: ADL-7 and GTEA_KITCHEN-6. To the best of our knowledge, they are the first to provide multi-scene and cross-site settings for UDA problem on first-person video action recognition, promoting diversity. They provide five more domains based on the original three from existing datasets, enriching data for this area. They are also compatible with existing datasets, ensuring scalability. First-person videos have unique challenges, i.e. actions tend to occur in hand-object interaction areas. Therefore, networks paying more attention to such areas can benefit common feature learning in UDA. Attention mechanisms can endow networks with the ability to allocate resources adaptively for the important parts of the inputs and fade out the rest. Hence, we introduce channel-temporal attention modules to capture the channel-wise and temporal-wise relationships and model their inter-dependencies important to this characteristic. Moreover, we propose a Channel-Temporal Attention Network (CTAN) to integrate these modules into existing architectures. CTAN outperforms baselines on the new datasets and one existing dataset, EPIC-8.

Machine learning has been widely used to develop classification models for autism spectrum disorder (ASD) using neuroimaging data. Recently, studies have shifted towards using large multi-site neuroimaging datasets to boost the clinical applicability and statistical power of results. However, the classification performance is hindered by the heterogeneous nature of agglomerative datasets. In this paper, we propose new methods for multi-site autism classification using the Autism Brain Imaging Data Exchange (ABIDE) dataset. We firstly propose a new second-order measure of functional connectivity (FC) named as Tangent Pearson embedding to extract better features for classification. Then we assess the statistical dependence between acquisition sites and FC features, and take a domain adaptation approach to minimize the site dependence of FC features to improve classification. Our analysis shows that 1) statistical dependence between site and FC features is statistically significant at the 5% level, and 2) extracting second-order features from neuroimaging data and minimizing their site dependence can improve over state-of-the-art (SOTA) classification results, achieving a classification accuracy of 73%. The code is available at https://github.com/kundaMwiza/fMRI-site-adaptation.

Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical settings, where it is imperative that erroneous predictions are caught and corrected. We propose Quantile Binning, a data-driven method to categorize predictions by uncertainty with estimated error bounds. Our framework can be applied to any continuous uncertainty measure, allowing straightforward identification of the best subset of predictions with accompanying estimated error bounds. We facilitate easy comparison between uncertainty measures by constructing two evaluation metrics derived from Quantile Binning. We compare and contrast three epistemic uncertainty measures (two baselines, and a proposed method combining aspects of the two), derived from two heatmap-based landmark localization model paradigms (U-Net and patch-based). We show results across three datasets, including a publicly available Cephalometric dataset. We illustrate how filtering out gross mispredictions caught in our Quantile Bins significantly improves the proportion of predictions under an acceptable error threshold. Finally, we demonstrate that Quantile Binning remains effective on landmarks with high aleatoric uncertainty caused by inherent landmark ambiguity, and offer recommendations on which uncertainty measure to use and how to use it. The code and data are available at https://github.com/schobs/qbin.

Pulmonary Arterial Hypertension (PAH) is a lifethreatening disorder. The prediction of mortality in PAH patients can play a crucial role in the clinical management of this disease. The prediction of mortality from one modality is a difficult task that may only provide limited performance. Therefore, we propose a multimodal learning approach in this work to predict one-year mortality in PAH patients. We have utilised three modalities, which include extracted numerical imaging features, echo report categorical features, and echo report text features from Electronic Health Records (EHRs) of patients. We have proposed a feature integration module to combine features from multiple modalities. The text features have been extracted from the echo reports using the Bidirectional Encoder Representations from Transformers (BERT). An attention mechanism and a weighted summation method are also adopted during the process of feature integration. We have performed different experiments to evaluate the performance of the proposed framework for mortality prediction. The experimental results indicate that we can achieve the best AUC score of 0.89 for predicting one-year mortality by combining all three modalities. The source code of this paper is available at https://github.com/Mdnaimulislam/MultimodalTab.

PyKale is a Python library for Knowledge-aware machine learning from multiple sources of data to enable/accelerate interdisciplinary research. It embodies green machine learning principles to reduce repetitions/redundancy, reuse existing resources, and recycle learning models across areas. We propose a pipeline-based application programming interface (API) so all machine learning workflows follow a standardized six-step pipeline. PyKale focuses on leveraging knowledge from multiple sources for accurate and interpretable prediction, particularly multimodal learning and transfer learning. To be more accessible, it separates code and configurations to enable non-programmers to configure systems without coding. PyKale is officially part of the PyTorch ecosystem and includes interdisciplinary examples in bioinformatics, knowledge graph, image/video recognition, and medical imaging: https://pykale.github.io/.

Watching online videos has become more and more popular and users tend to watch videos based on their personal tastes and preferences. Providing a customized ranking list to maximize the user's satisfaction has become increasingly important for online video platforms. Existing personalized search methods (PSMs) train their models with user feedback information (e.g. clicks). However, we identified that such feedback signals may indicate attractiveness but not necessarily indicate relevance in video search. Besides, the click data and user historical information are usually too sparse to train a good PSM, which is different from the conventional Web search containing users' rich historical information. To address these concerns, in this paper we propose a multi-task graph neural network architecture for personalized video search (MGNN-PVS) that can jointly model user's click behaviour and the relevance between queries and videos. To relieve the sparsity problem and learn better representation for users, queries and videos, we develop an efficient and novel GNN architecture based on neighborhood sampling and hierarchical aggregation strategy by leveraging their different hops of neighbors in the user-query and query-document click graph. Extensive experiments on a major commercial video search engine show that our model significantly outperforms state-of-the-art PSMs, which illustrates the effectiveness of our proposed framework.

Graph convolutional network (GCN) is an effective neural network model for graph representation learning. However, standard GCN suffers from three main limitations: (1) most real-world graphs have no regular connectivity and node degrees can range from one to hundreds or thousands, (2) neighboring nodes are aggregated with fixed weights, and (3) node features within a node feature vector are considered equally important. Several extensions have been proposed to tackle the limitations respectively. This paper focuses on tackling all the proposed limitations. Specifically, we propose a new node-feature convolutional (NFC) layer for GCN. The NFC layer first constructs a feature map using features selected and ordered from a fixed number of neighbors. It then performs a convolution operation on this feature map to learn the node representation. In this way, we can learn the usefulness of both individual nodes and individual features from a fixed-size neighborhood. Experiments on three benchmark datasets show that NFC-GCN consistently outperforms state-of-the-art methods in node classification.

Independent Component Analysis (ICA) is a fundamental method for Blind Source Separation (BSS). Classical ICA takes data matrix input formed by vector data. This paper focuses on ICA for BSS with third-order data tensor input formed by matrix data, such as 2D images. Two approaches exist for this problem. The first approach reshapes each matrix into a vector to apply classical ICA, with structural information lost. The second approach unfolds a data tensor into a data matrix along different modes to perform classical ICA mode-wise, which partially preserves structures but has strong or ill BSS assumptions. This paper proposes a third approach via RAndom Matrix ICA (RAMICA) modeling. RAMICA works on data tensor directly, without vectorization or unfolding, and preserves row or column structures under more general BSS assumptions. We develop the RAMICA model, algorithm, and related theories via defining new statistics for random matrices and new procedures for whitening and independent component estimation. We study the identifiability, higher-order extension, and relationships with existing methods. Experiments on both synthetic and real data show superior BSS performance of RAMICA over competing methods and offer insights on the trade-offs between different factors.

Drug-target interaction (DTI) prediction is important in drug discovery and chemogenomics studies. Machine learning, particularly deep learning, has advanced this area significantly over the past few years. However, a significant gap between the performance reported in academic papers and that in practical drug discovery settings, e.g. the random-split-based evaluation strategy tends to be too optimistic in estimating the prediction performance in real-world settings. Such performance gap is largely due to hidden data bias in experimental datasets and inappropriate data split. In this paper, we construct a low-bias DTI dataset and study more challenging data split strategies to improve performance evaluation for real-world settings. Specifically, we study the data bias in a popular DTI dataset, BindingDB, and re-evaluate the prediction performance of three state-of-the-art deep learning models using five different data split strategies: random split, cold drug split, scaffold split, and two hierarchical-clustering-based splits. In addition, we comprehensively examine six performance metrics. Our experimental results confirm the overoptimism of the popular random split and show that hierarchical-clustering-based splits are far more challenging and can provide potentially more useful assessment of model generalizability in real-world DTI prediction settings.

Predicting drug-drug interactions (DDIs) becomes an increasingly important problem in the computational domain, due to the acceleration of drug discovery and the high costs of solving this task through in vitro experiments. It can be modelled as a link prediction task on a DDIs network, where each node represents a drug and each link represents a meaningful interaction between a pair of drugs. Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification and graph classification and consequently there is an increasing interest into their application on link prediction. The well-established GNN-based encoder-decoder framework requires negative links (i.e. non-interactions) for training, as it is performed in a supervised fashion. Most of the current DDI datasets do not provide laboratory-confirmed negative interactions (i.e. non-interacting pairs of drugs) and so negative sampling from the unobserved links becomes an exclusively computational task.There is a lack of research investigating the quality and importance of negative sampling techniques when predicting on homogeneous graphs. This paper focuses on improving negative sampling in GNNs on a homogeneous drug-drug interaction graph. An analysis into the effect of negative sampling on predicting DDIs in such a network is carried out and current approaches are formalised and tested. Following this, LANS, a novel Loss-based Adaptive Negative Sampling technique, is introduced. The lightweight LANS method achieves an increase in performance of 16.49% in the Hits@20 metric without adding any trainable parameters to the architecture. Finally, some of its limitations are identified and potential improvements are proposed.

Subspace learning for tensors attracts increasing interest in recent years, leading to the development of multilinear extensions of principal component analysis (PCA) and probabilistic PCA (PPCA). Existing multilinear PPCAs are based on the Tucker or CANDECOMP/PARAFAC (CP) models. Although both kinds of multilinear PPCAs have shown their effectiveness in dealing with tensors, they also have their own limitations. Tucker-based multilinear PPCAs have a restrictive subspace representation and suffer from rotational ambiguity, while CP-based ones are more prone to overfitting. To address these problems, we propose probabilistic rank-one tensor analysis (PROTA), a CP-based multilinear PPCA. PROTA has a more flexible subspace representation than Tucker-based PPCAs, and avoids rotational ambiguity. To alleviate overfitting for CP-based PPCAs, we propose two simple and effective regularization strategies, named as concurrent regularizations (CRs). By adjusting the noise variance or the moments of latent features, our strategies concurrently and coherently penalize the entire subspace. This relaxes unnecessary scale restrictions and gains more flexibility in regularizing CP-based PPCAs. To take full advantage of the probabilistic framework, we further propose a Bayesian treatment of PROTA, which achieves both automatic feature determination and robustness against overfitting. Experiments on synthetic and real-world datasets demonstrate the superiority of PROTA in subspace estimation and classification, as well as the effectiveness of CRs in alleviating overfitting.

The increasing of public neuroimaging datasets opens a door to analyzing homogeneous human brain conditions across datasets by transfer learning (TL). However, neuroimaging data are high-dimensional, noisy, and with small sample sizes. It is challenging to learn a robust model for data across different cognitive experiments and subjects. A recent TL approach minimizes domain dependence to learn common cross-domain features, via the Hilbert-Schmidt Independence Criterion (HSIC). Inspired by this approach and the multi-source TL theory, we propose a Side Information Dependence Regularization (SIDeR) learning framework for TL in brain condition decoding. Specifically, SIDeR simultaneously minimizes the empirical risk and the statistical dependence on the domain side information, to reduce the theoretical generalization error bound. We construct 17 brain decoding TL tasks using public neuroimaging data for evaluation. Comprehensive experiments validate the superiority of SIDeR over ten competing methods, particularly an average improvement of 15.6% on the TL tasks with multi-source experiments.

Neighborhood aggregation is a key step in Graph Convolutional Networks (GCNs) for graph representation learning. Two commonly used aggregators, sum and mean, are designed with the homophily assumption that connected nodes are likely to share the same label. However, real-world graphs are noisy and adjacent nodes do not necessarily imply similarity.Learnable aggregators are proposed in Graph Attention Network (GAT) and Learnable Graph Convolutional Layer (LGCL). However, GAT considers node importance but not the importance of different features. The convolution aggregator in LGCL considers feature importance but it can not directly operate on graphs due to the irregular connectivity and lack of orderliness. In this paper, we firstly unify the current learnable aggregators in a framework: Learnable Aggregator for GCN (LA-GCN) by introducing a shared auxiliary model that provides a customized schema in neighborhood aggregation. Under this framework, we propose a new model called LA-GCNMask consisting of a new aggregator function,mask aggregator. The auxiliary model learns a specific mask for each neighbor of a given node, allowing both node-level and feature-level attention. This mechanism learns to assign different importance to both nodes and features for prediction, which provides interpretable explanations for prediction and increases the model robustness. Experiments on seven graphs for node classification and graph classification tasks show that LA-GCNMask outperforms the state-of-the-art methods. Moreover, our aggregator can identify both the important nodes and node features simultaneously, which provides a quantified understanding of the relationship between input nodes and the prediction. We further conduct experiments on noisy graphs to evaluate the robustness of our model. Experiments show that LA-GCNMask consistently outperforms the state-of-the-art methods, with up to 15% improvements in terms of accuracy compared to the second best.

Multidimensional data (i.e., tensors) with missing entries are common in practice. Extracting features from incomplete tensors is an important yet challenging problem in many fields such as machine learning, pattern recognition, and computer vision. Although the missing entries can be recovered by tensor completion techniques, these completion methods focus only on missing data estimation instead of effective feature extraction. To the best of our knowledge, the problem of feature extraction from incomplete tensors has yet to be well explored in the literature. In this paper, we therefore tackle this problem within the unsupervised learning environment. Specifically, we incorporate low-rank tensor decomposition with feature variance maximization (TDVM) in a unified framework. Based on orthogonal Tucker and CP decompositions, we design two TDVM methods, TDVM-Tucker and TDVM-CP, to learn low-dimensional features viewing the core tensors of the Tucker model as features and viewing the weight vectors of the CP model as features. TDVM explores the relationship among data samples via maximizing feature variance and simultaneously estimates the missing entries via low-rank Tucker/CP approximation, leading to informative features extracted directly from observed entries. Furthermore, we generalize the proposed methods by formulating a general model that incorporates feature regularization into low-rank tensor approximation. In addition, we develop a joint optimization scheme to solve the proposed methods by integrating the alternating direction method of multipliers with the block coordinate descent method. Finally, we evaluate our methods on six real-world image and video data sets under a newly designed multiblock missing setting. The extracted features are evaluated in face recognition, object/action classification, and face/gait clustering. Experimental results demonstrate the superior performance of the proposed methods compared with the state-of-the-art approaches.

Completing a matrix from a small subset of its entries, i.e., matrix completion is a challenging problem arising from many real-world applications, such as machine learning and computer vision. One popular approach to solve the matrix completion problem is based on low-rank decomposition/factorization. Low-rank matrix decomposition-based methods often require a prespecified rank, which is difficult to determine in practice. In this paper, we propose a novel low-rank decomposition-based matrix completion method with automatic rank estimation. Our method is based on rank-one approximation, where a matrix is represented as a weighted summation of a set of rank-one matrices. To automatically determine the rank of an incomplete matrix, we impose L1-norm regularization on the weight vector and simultaneously minimize the reconstruction error. After obtaining the rank, we further remove the L1-norm regularizer and refine recovery results. With a correctly estimated rank, we can obtain the optimal solution under certain conditions. Experimental results on both synthetic and real-world data demonstrate that the proposed method not only has good performance in rank estimation, but also achieves better recovery accuracy than competing methods.

Embedded feature selection is effective when both prediction and interpretation are needed. The Lasso and its extensions are standard methods for selecting a subset of features while optimizing a prediction function. In this paper, we are interested in embedded feature selection for multidimensional data, wherein (1) there is no need to reshape the multidimensional data into vectors and (2) structural information from multiple dimensions are taken into account. Our main contribution is a new method called Regularized multilinear regression and selection (Remurs) for automatically selecting a subset of features while optimizing prediction for multidimensional data. Both nuclear norm and the ℓ1-norm are carefully incorporated to derive a multi-block optimization algorithm with proved convergence. In particular, Remurs is motivated by fMRI analysis where the data are multidimensional and it is important to find the connections of raw brain voxels with functional activities. Experiments on synthetic and real data show the advantages of Remurs compared to Lasso, Elastic Net, and their multilinear extensions.

Canonical Correlation Analysis (CCA) is a classical technique for two-view correlation analysis, while Probabilistic CCA (PCCA) provides a generative and more general viewpoint for this task. Recently, PCCA has been extended to bilinear cases for dealing with two-view matrices in order to preserve and exploit the matrix structures in PCCA. However, existing bilinear PCCAs impose restrictive model assumptions for matrix structure preservation, sacrificing generative correctness or model flexibility. To overcome these drawbacks, we propose BPCCA, a new bilinear extension of PCCA, by introducing a hybrid joint model. Our new model preserves matrix structures indirectly via hybrid vector-based and matrix-based concatenations. This enables BPCCA to gain more model flexibility in capturing two-view correlations and obtain close-form solutions in parameter estimation. Experimental results on two real-world applications demonstrate the superior performance of BPCCA over competing methods.

Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition. However, this approach often requires the determination of a tensor rank a priori. This rank estimation problem is difficult in practice. Several Bayesian solutions have been proposed but they often under/over-estimate the tensor rank while being quite slow. To address this problem of rank estimation with missing entries, we view the weight vector of the orthogonal CP decomposition of a tensor to be analogous to the vector of singular values of a matrix. Subsequently, we define a new CP-based tensor nuclear norm as the $L_1$-norm of this weight vector. We then propose Tensor Rank Estimation based on $L_1$-regularized orthogonal CP decomposition (TREL1) for both CP-rank and Tucker-rank. Specifically, we incorporate a regularization with CP-based tensor nuclear norm when minimizing the reconstruction error in TC to automatically determine the rank of an incomplete tensor. Experimental results on both synthetic and real data show that: 1) Given sufficient observed entries, TREL1 can estimate the true rank (both CP-rank and Tucker-rank) of incomplete tensors well; 2) The rank estimated by TREL1 can consistently improve recovery accuracy of decomposition-based TC methods; 3) TREL1 is not sensitive to its parameters in general and more efficient than existing rank estimation methods.

Independent Component Analysis (ICA) is a classical method for Blind Source Separation (BSS). In this paper, we are interested in ICA in the presence of noise, i.e., the noisy ICA problem. Pseudo-Euclidean Gradient Iteration (PEGI) is a recent cumulant-based method that defines a pseudo Euclidean inner product to replace a quasi-whitening step in Gaussian noise invariant ICA. However, PEGI has two major limitations: 1) the pseudo Euclidean inner product is improper because it violates the positive definiteness of inner product; 2) the inner product matrix is orthogonal by design but it has gross errors or imperfections due to sample-based estimation. This paper proposes a new cumulant-based ICA method named as PIMD to address these two problems. We first define a Proper Inner product (PI) with proved positive definiteness and then relax the centering preprocessing step to a mean displacement (MD) step. Both PI and MD aim to improve the orthogonality of inner product matrix and the recovery of independent components (ICs) in sample-based estimation. We adopt a gradient iteration step to find the ICs for PIMD. Experiments on both synthetic and real data show the respective effectiveness of PI and MD as well as the superiority of PIMD over competing ICA methods. Moreover, MD can improve the performance of other ICA methods as well.

Independent component analysis (ICA) is an important unsupervised learning method. Most popular ICA methods use kurtosis as a metric of non-Gaussianity to maximize, such as FastICA and JADE.However, their assumption of kurtosic sources may not always be satisfied in practice. For weak-kurtosic but skewed sources, kurtosis-based methods could fail while skewness-based methods seem more promising, where skewness is another non-Gaussianity metric measuring the non-symmetry of signals. Partly due to the common assumption of signal symmetry, skewness-based ICA has not been systematically studied in spite of some existing works. In this paper, we take a systematic approach to develop EcoICA, a new skewness-based ICA method for weak-kurtosic but skewed sources. Specifically, we design a new cumulant operator, define its eigenvalues and eigenvectors, reveal their connections with the ICA model to formulate the EcoICA problem, and use Jacobi method to solve it. Experiments on both synthetic and real data show the superior performance of EcoICA over existing kurtosis-based and skewness-based methods for skewed sources. In particular, EcoICA is less sensitive to sample size, noise, and outlier than other methods. Studies on face recognition further confirm the usefulness of EcoICA in classification.

For big, high-dimensional dense features, it is important to learn compact binary codes or compress them for greater memory efficiency. This paper proposes a Binarized Multilinear PCA (BMP) method for this problem with Free-Form Reshaping (FFR) of such features to higher-order tensors, lifting the structure-modelling restriction in traditional tensor models. The reshaped tensors are transformed to a subspace using multilinear PCA. Then, we unsupervisedly select features and supervisedly binarize them with a minimum-classification-error scheme to get compact binary codes. We evaluate BMP on two scene recognition datasets against state-of-the-art algorithms. The FFR works well in experiments. With the same number of compression parameters (model size), BMP has much higher classification accuracy. To achieve the same accuracy or compression ratio, BMP has an order of magnitude smaller number of compression parameters. Thus, BMP has great potential in memory-sensitive applications such as mobile computing and big data analytics.

This paper presents a novel approach for the visualization and clustering of crowd video contents by using multilinear principal component analysis (MPCA). In contrast to feature-point-based approach and frame-based dimensionality reduction approach, the proposed method maps each short video segment to a point in MPCA subspace to take temporal information into account naturally through tensorial representations. Specifically, MPCA projects each short segment of a video to a low-dimensional tensor first. A few MPCA features are then selected according to the variance captured as the final representation. Thus, a video is visualized as a trajectory in MPCA subspace. The trajectory generated enables visual interpretation of video content in a compact space as well as visual clustering of video events. The proposed method is evaluated on the PETS 2009 datasets through comparison with three existing methods for video visualization. The MPCA visualization shows superior performance in clustering segments of the same event as well as identifying the transitions between events.

Biometric encryption (BE) has recently been identified as a promising paradigm to deliver security and privacy, with unique technical merits and encouraging social implications. An integral component in BE is a key binding method, which is the process of securely combining a signal, containing sensitive information to be protected (i.e., the key), with another signal derived from physiological features (i.e., the biometric). A challenge to this approach is the high degree of noise and variability present in physiological signals. As such, fuzzy methods are needed to enable proper operations, with adequate performance results in terms of false acceptance rate and false rejection rate. In this work, the focus will be on a class of fuzzy key binding methods based on dirty paper coding known as quantization index modulation. While the methods presented are applicable to a wide range of biometric modalities, the face biometric is selected for illustrative purposes, in evaluating the QIM-based solutions for BE systems. Performance evaluation of the investigated methods is reported using data from the CMU PIE face database.

This paper proposes an uncorrelated multilinear discriminant analysis (UMLDA) framework for the recognition of multidimensional objects, known as tensor objects. Uncorrelated features are desirable in recognition tasks since they contain minimum redundancy and ensure independence of features. The UMLDA aims to extract uncorrelated discriminative features directly from tensorial data through solving a tensor-to-vector projection. The solution consists of sequential iterative processes based on the alternating projection method, and an adaptive regularization procedure is incorporated to enhance the performance in the small sample size (SSS) scenario. A simple nearest-neighbor classifier is employed for classification. Furthermore, exploiting the complementary information from differently initialized and regularized UMLDA recognizers, an aggregation scheme is adopted to combine them at the matching score level, resulting in enhanced generalization performance while alleviating the regularization parameter selection problem. The UMLDA-based recognition algorithm is then empirically shown on face and gait recognition tasks to outperform four multilinear subspace solutions (MPCA, DATER, GTDA, TR1DA) and four linear subspace solutions (Bayesian, LDA, ULDA, R-JD-LDA).

This paper proposes an uncorrelated multilinear principal component analysis (UMPCA) algorithm for unsupervised subspace learning of tensorial data. It should be viewed as a multilinear extension of the classical principal component analysis (PCA) framework. Through successive variance maximization, UMPCA seeks a tensor-to-vector projection (TVP) that captures most of the variation in the original tensorial input while producing uncorrelated features. The solution consists of sequential iterative steps based on the alternating projection method. In addition to deriving the UMPCA framework, this work offers a way to systematically determine the maximum number of uncorrelated multilinear features that can be extracted by the method. UMPCA is compared against the baseline PCA solution and its five state-of-the-art multilinear extensions, namely two-dimensional PCA (2DPCA), concurrent subspaces analysis (CSA), tensor rank-one decomposition (TROD), generalized PCA (GPCA), and multilinear PCA (MPCA), on the tasks of unsupervised face and gait recognition. Experimental results included in this paper suggest that UMPCA is particularly effective in determining the low-dimensional projection space needed in such recognition tasks.

Tensorial data are frequently encountered in various machine learning tasks today and dimensionality reduction is one of their most important applications. This paper extends the classical principal component analysis (PCA) to its multilinear version by proposing a novel unsupervised dimensionality reduction algorithm for tensorial data, named as uncorrelated multilinear PCA (UMPCA). UMPCA seeks a tensor-to-vector projection that captures most of the variation in the original tensorial input while producing uncorrelated features through successive variance maximization. We evaluate the UMPCA on a second-order tensorial problem, face recognition, and the experimental results show its superiority, especially in low-dimensional spaces, through the comparison with three other PCA-based algorithms.

This paper introduces a multilinear principal component analysis (MPCA) framework for tensor object feature extraction. Objects of interest in many computer vision and pattern recognition applications, such as 2-D/3-D images and video sequences are naturally described as tensors or multilinear arrays. The proposed framework performs feature extraction by determining a multilinear projection that captures most of the original tensorial input variation. The solution is iterative in nature and it proceeds by decomposing the original problem to a series of multiple projection subproblems. As part of this work, methods for subspace dimensionality determination are proposed and analyzed. It is shown that the MPCA framework discussed in this work supplants existing heterogeneous solutions such as the classical principal component analysis (PCA) and its 2-D variant (2-D PCA). Finally, a tensor object recognition system is proposed with the introduction of a discriminative tensor feature selection mechanism and a novel classification strategy, and applied to the problem of gait recognition. Results presented here indicate MPCA's utility as a feature extraction tool. It is shown that even without a fully optimized design, an MPCA-based gait recognition module achieves highly competitive performance and compares favorably to the state-of-the-art gait recognizers.

This paper presents a localized coarse-to-fine algorithm for efficient and accurate pedestrian localization and silhouette extraction for the gait challenge data sets. The coarse detection phase is simple and fast. It locates the target quickly based on temporal differences and some knowledge on the human target. Based on this coarse detection, the fine detection phase applies a robust background subtraction algorithm to the coarse target regions and the detection obtained is further processed to produce the final results. This algorithm has been tested on 285 outdoor sequences from the gait challenge data sets, with wide variety of capture conditions. The pedestrian targets are localized very well and silhouettes extracted resemble the manually labeled silhouettes closely

In this letter, we present a novel objective distortion measure for binary document images. This measure is based on the reciprocal of distance that is straightforward to calculate. Our results show that the proposed distortion measure matches well to subjective evaluation by human visual perception.

This paper presents a watermarking algorithm for binary images. The original binary image is blurred to a gray-level image and we embed the watermark by biasing the threshold in binarization. A loop is used to control the quality of watermarked images and robustness, and a key is generated for extraction. We employ error correction codes to reduce extraction error. This algorithm can be applied to general binary images except dithered images. Experiments show that the distortion in the watermarked image is not obtrusive and the algorithm provides some degree of robustness.

Minutiae extraction is a crucial step in an automatic fingerprint identification system. However, the presence of noise in poor-quality images causes a large number of extraction errors, including the dropping of true minutiae and production of false minutiae. A study on these errors reveals that postprocessing is effective in removing false minutiae while keeping true ones. Furthermore, the overall processing efficiency could be improved because of the reduction in total minutia number. In this paper, we present a novel fingerprint image postprocessing algorithm. It is developed based on several rules, which are generalized through a study on the errors that commonly occur in minutiae extraction and their effects on the overall verification performance. Thorough experimental tests demonstrate the proposed postprocessing algorithm to be both effective and efficient.