Graph-based multiview clustering methods have attracted much attention because of their ability to mine nonlinear structural information among instances. Although they perform well in many scenarios, they consume a lot of computational resources when dealing with large-scale multiview scenarios. To address this issue, we present a new insight into the anchor graph mechanism and propose a novel Nonnegative Anchor Graph Reconstruction (NAGR) model. NAGR introduces the sparse similarity graph into the symmetric matrix factorization and gets the nonnegative representation that retains the graph structural information. Thereafter, we develop a novel Efficient Multiview nonnegative Representation learning framework with Correntropy and Anchor graph (EMR-CA), which integrates multiview anchor graph reconstruction and consensus nonnegative representation learning into a unified framework. EMR-CA uses multiview anchor graph reconstruction to learn consensus nonnegative representation, where correntropy rather than F-norm is used as the approximation measurement criterion. Specifically, normalized anchor graphs of different views are decomposed into a consensus nonnegative representation and multiple view-specific representations, where the consensus representation retains the neighbor graph information between multiview instances and representative anchors on different views. Finally, the effectiveness of the proposed EMR-CA framework is verified by theoretical analysis and experimental results on large-scale realistic multiview scenarios.

Linear discriminant analysis (LDA) is a popular technique to learn the most discriminative features for multi-class classification. A vast majority of existing LDA algorithms are prone to be dominated by the class with very large deviation from the others, i.e., edge class, which occurs frequently in multi-class classification. First, the existence of edge classes often makes the total mean biased in the calculation of between-class scatter matrix. Second, the exploitation of ℓ2-norm based between-class distance criterion magnifies the extremely large distance corresponding to edge class. In this regard, a novel self-weighted robust LDA with ℓ2,1-norm based pairwise between-class distance criterion, called SWRLDA, is proposed for multi-class classification especially with edge classes. SWRLDA can automatically avoid the optimal mean calculation and simultaneously learn adaptive weights for each class pair without setting any additional parameter. An efficient re-weighted algorithm is exploited to derive the global optimum of the challenging ℓ2,1-norm maximization problem. The proposed SWRLDA is easy to implement and converges fast in practice. Extensive experiments demonstrate that SWRLDA performs favorably against other compared methods on both synthetic and real-world datasets while presenting superior computational efficiency in comparison with other techniques.

Recently, deep convolution neural networks have achieved remarkable performance in the task of single image super-resolution (SISR). However, effectiveness of existing networks highly relies on their receptive field, which always increases with the depth of the network. In this work, we propose a novel module, named as residual group, to effectively learn feature maps by using dynamic receptive field. This residual group firstly uses a selective kernel convolution layer to dynamically learn multi-scale information from its input features. Then, several residual blocks are employed to further refine the learned feature. In addition, we also propose a selective feature fusion module to fuse appearance information in multi-level features. Within this module, the low-level features and high-level features are selectively fused to complement the high-level ones. Finally, by combining these two methods, we introduce a multi-level feature fusion network (MLFFN) for single image super-resolution (SISR). Through comprehensive experiments, we demonstrate that the proposed MLFFN achieves state-of-the-art performance both quantitatively and qualitatively.

The effective training of supervised Person Re-identification (Re-ID) models requires sufficient pairwise labeled data. However, when there is limited annotation resource, it is difficult to collect pairwise labeled data. We consider a challenging and practical problem called Early Active Learning, which is applied to the early stage of experiments when there is no pre-labeled sample available as references for human annotating. Previous early active learning methods suffer from two limitations for Re-ID. First, these instance-based algorithms select instances rather than pairs, which can result in missing optimal pairs for Re-ID. Second, most of these methods only consider the representativeness of instances, which can result in selecting less diverse and less informative pairs. To overcome these limitations, we propose a novel pair-based active learning for Re-ID. Our algorithm selects pairs instead of instances from the entire dataset for annotation. Besides representativeness, we further take into account the uncertainty and the diversity in terms of pairwise relations. Therefore, our algorithm can produce the most representative, informative, and diverse pairs for Re-ID data annotation. Extensive experimental results on five benchmark Re-ID datasets have demonstrated the superiority of the proposed pair-based early active learning algorithm.

In this paper, we focus on the multi-view clustering problem. A novel multi-view clustering framework, called multi-view subspace clustering based on tensor Schatten-p norm (MVSC-TSP), is proposed for clustering task. In our method, the tensor Schatten-p norm, which is based on tensor singular value decomposition, is utilized to explore the global low-rank structure of multi-view self-representations. Since 0<; p<; 1, using tensor Schatten-p norm to relax the tensor multi-rank is more effective than the commonly used tensor nuclear norm. Furthermore, we present a new generalized tensor soft thresholding algorithm to solve the tensor Schatten-p norm minimization problem. Based on this, the proposed non-convex optimal problem can be efficiently solved by the alternating direction method of multipliers. Experimental results on image clustering demonstrate that the proposed method is superior to the state-of-the-art methods in term of various evaluation metrics.

Recently, group sparse representation which is based on a hypothesis about correlation of coefficient variables has attracted much attention due to its effectiveness and robustness in dictionary learning. Traditional group sparse representation methods use ℓ2,1norm to enforce the estimation of models with joint sparsity patterns, which often leads to over-punishment phenomenon. To solve this issue, we replace ℓ2,1with non-convex surrogate of ℓ2,1, and give a general solver for the corresponding optimization algorithm. Experimental results confirm the effectiveness of our proposed method.

Single image haze removal is a challenging ill-posed problem in computer vision. Instead of leveraging the traditional model or handcrafted image priors, an end-to-end multi-scale convolutional neural network is proposed for single image haze removal task by directly mapping the hazy image to its corresponding haze-free image. To better retain the coarse and fine information, a multi-scale block is elaborated and embedded into the proposed architecture. This block can extract the feature at varying scales with a model size that is as small as possible. The global skip connection is adopted to promote the model performance. Extensive experiment results demonstrate that the proposed network outperforms the state-of-the-art single image haze removal algorithms on both synthetical and real-world images. In addition, the size of the model in this paper dominates among the high performance methods based on convolutional neural networks.

In this paper, we propose a new single-image dehazing method for synthetic and real-world hazy images. Based on the color attenuation prior, this proposed dehazing method improves it in two aspects. First, we estimate the atmospheric light with the haze-lines prior, which is based on the observation that pixel values of a hazy image can be modeled as lines in the RGB color space that intersects at the air-light. Second, the dynamic scattering coefficient, which is an exponential function of image depth, is proposed to replace the constant scattering coefficient. Experimental results demonstrate that the dehazed image of proposed algorithm is clearer and more natural than that of the color attenuation prior. The proposed algorithm can effectively improve the effect of dehazing.

In this paper, we propose a novel unified optimization framework for semi-supervised dictionary learning, which optimizes a graph Laplacian component and the dictionary simultaneously. In the framework, the graph Laplacian is defined on the atoms and the corresponding sparse codings. Since the atoms are more concise and representative than the original training samples, the constructed graph Laplacian can not only effectively capture the manifold structure of training samples, but also be more robust to noise and outliers. Moreover, the dictionary and the graph Laplacian can facilitate each other during the learning iterations. We derive an efficient algorithm by combining the block coordinate descent method with the alternating direction method of multipliers to solve the unified optimization problem. Extensive experimental evaluation on several challenging datasets demonstrates the superior performance of the proposed method.

Manifold learning is an important topic in pattern recognition and computer vision. However, most manifold learning algorithms implicitly assume the data are aligned on a single manifold, which is too strict in actual applications. Isometric feature mapping (Isomap), as a promising manifold learning method, fails to work on data which distribute on clusters in a single manifold or manifolds. In this paper, we propose a new multi-manifold learning algorithm (M-Isomap). The algorithm first discovers the data manifolds and then reduces the dimensionality of the manifolds separately. Meanwhile, a skeleton representing the global structure of whole data set is built and kept in low-dimensional space. Secondly, by referring to the low-dimensional representation of the skeleton, the embeddings of the manifolds are relocated to a global coordinate system. Compared with previous methods, these algorithms can keep both of the intra and inter manifolds geodesics faithfully. The features and effectiveness of the proposed multi-manifold learning algorithms are demonstrated and compared through experiments.

Manifold learning is an important feature extraction approach in data mining. This paper presents a new semi-supervised manifold learning algorithm, called Multi-Manifold Discriminative Analysis (Multi-MDA). The proposed method is designed to explore the discriminative information hidden in geodesic distances. The main contributions of the proposed method are: 1) we propose a semi-supervised graph construction method which can effectively capture the multiple manifolds structure of the data, 2) each data point is replaced with an associated feature vector whose elements are the graph distances from it to the other data points. Information of the nonlinear structure is contained in the feature vectors which are helpful for classification, 3) we propose a new semi-supervised linear dimension reduction method for feature vectors which introduces the class information into the manifold learning process and establishes an explicit dimension reduction mapping. Experiments on benchmark data sets are conducted to show the effectiveness of the proposed method.

Due to the increasing demands for network security, distributed intrusion detection has become a hot research topic in computer science. However, the design and maintenance of the intrusion detection system (IDS) is still a challenging task due to its dynamic, scalability, and privacy properties. In this paper, we propose a distributed IDS framework which consists of the individual and global models. Specifically, the individual model for the local unit derives from Gaussian Mixture Model based on online Adaboost algorithm, while the global model is constructed through the PSO-SVM fusion algorithm. Experimental results demonstrate that our approach can achieve a good detection performance while being trained online and consuming little traffic to communicate between local units.

Currently most link-related applications treat all links in the same web page to be identical. One link-related application usually requires one certain property of hyperlinks but actually not all links have this property or they have this property on different levels. Based on a study of how human users judge the links, the idea of the link function classification (LFC) is introduced in this paper. The link functions reflect the purpose that links are created by web page designers and the way they are used by viewers. Links in a certain function class imply one certain relationship between the adjacent pages, and thus they can be assumed to have similar properties. An algorithm is proposed to analyze the link functions based on both vision and structure features which simulates the reaction on the links of human users. Current applications can be enhanced by LFC with a more accurate modeling of the web graph. New mining methods can be also developed by making more and stronger assumptions on links within each function class due to the purer property set they share.