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Convolutional Sparse and low-rank coding-Based Image Decomposition

IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第5期27卷 2121-2133页

作者： Zhang, He Patel, Vishal M. Rutgers Univ New Brunswick Dept Elect & Comp Engn New Brunswick NJ 08901 USA

We propose novel convolutional sparse and low-rank coding-based methods for cartoon and texture decomposition. In our method, we first learn a set of generic filters that can efficiently represent cartoon-and texture-type images. Then, using these learned filters, we propose two optimization frameworks to decompose a given image into cartoon and texture components: convolutional sparse coding-based image decomposition;and convolutional low-rank coding-based image decomposition. By working directly on the whole image, the proposed image separation algorithms do not need to divide the image into overlapping patches for leaning local dictionaries. The shift-invariance property is directly modeled into the objective function for learning filters. Extensive experiments show that the proposed methods perform favorably compared with state-of-theart image separation methods.

关键词： Image decomposition convolutional coding low-rank coding sparse coding

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Dictionary Learning With low-rank coding Coefficients for Tensor Completion

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第2期34卷 932-946页

作者： Jiang, Tai-Xiang Zhao, Xi-Le Zhang, Hao Ng, Michael K. Southwestern Univ Finance & Econ Sch Econ Informat Engn FinTech Innovat Ctr Chengdu 611130 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Math Sci Res Ctr Image & Vis Comp Chengdu 611731 Peoples R China Univ Hong Kong Dept Math Pokfulam Hong Kong Peoples R China

In this article, we propose a novel tensor learning and coding model for third-order data completion. The aim of our model is to learn a data-adaptive dictionary from given observations and determine the coding coefficients of third-order tensor tubes. In the completion process, we minimize the low-rankness of each tensor slice containing the coding coefficients. By comparison with the traditional predefined transform basis, the advantages of the proposed model are that: 1) the dictionary can be learned based on the given data observations so that the basis can be more adaptively and accurately constructed and 2) the low-rankness of the coding coefficients can allow the linear combination of dictionary features more effectively. Also we develop a multiblock proximal alternating minimization algorithm for solving such tensor learning and coding model and show that the sequence generated by the algorithm can globally converge to a critical point. Extensive experimental results for real datasets such as videos, hyperspectral images, and traffic data are reported to demonstrate these advantages and show that the performance of the proposed tensor learning and coding method is significantly better than the other tensor completion methods in terms of several evaluation metrics.

关键词： Tensors Encoding Transforms Dictionaries Discrete Fourier transforms Machine learning Electron tubes Dictionary learning low-rank coding tensor completion tensor singular value decomposition (t-SVD)

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Multi-View low-rank coding-Based Network Data De-Anonymization

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IEEE ACCESS 2020年 8卷 94575-94593页

作者： Xian, Xingping Wu, Tao Qiao, Shaojie Wang, Wei Liu, Yanbing Han, Nan Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Sch Cyber Secur & Informat Law Chongqing 400065 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Sichuan Univ Inst Cybersecur Chengdu 610065 Peoples R China Chongqing Univ Posts & Telecommun Chongqing Engn Lab Internet & Informat Secur Chongqing 400065 Peoples R China Chengdu Univ Informat Technol Sch Management Chengdu 610103 Peoples R China

Social networks are extensively exploited by third-party consumers such as researchers and advertisers to understand user characteristics and behaviors. In general, before network data is published, sensitive relationships should be anonymized to prevent the compromise of individual privacy. To quantify the guarantee level of privacy-preserving mechanisms and mitigate users' privacy concerns, numerous studies concerning network data de-anonymization have been carried out. However, most existing studies focus on single-view data, and privacy protection for multi-view data that is ubiquitous in the era of big data has not been yet extensively explored. In this study, we are interested in answering the following question: Are the traditional privacy protection methods still valid for the anonymization of multi-view data? In this study, we propose a Multi-View low-rank coding (MVLRC) based network data de-anonymization framework to assess the vulnerability of privacy protection techniques by accurately reconstructing a large portion of the original data. Specifically, the framework assumes that in principle, the target and auxiliary networks have common structural patterns, and they can be modeled together to infer the hidden structure of the target network. The essential components of our work include the following: (1) a robust network representation model for structural pattern learning;(2) the network representation based multi-view modeling of target network and auxiliary network;(3) the inference of the anonymized links via target network reconstruction. Experimental results on synthetic networks and three real-world networks demonstrate that auxiliary networks can be utilized by malicious adversaries for privacy inference attacks. Thus, the privacy protection of multi-view network data needs more sophisticated anonymization techniques.

关键词： Data privacy Social network services Privacy Encoding Perturbation methods Data models Telecommunications Privacy preserving de-anonymization network data multi-view learning low-rank coding

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Self-Taught low-rank coding for Visual Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018年第3期29卷 645-656页

作者： Li, Sheng Li, Kang Fu, Yun Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Dept Elect & Comp Engn Boston MA 02115 USA

The lack of labeled data presents a common challenge in many computer vision and machine learning tasks. Semisupervised learning and transfer learning methods have been developed to tackle this challenge by utilizing auxiliary samples from the same domain or from a different domain, respectively. Self-taught learning, which is a special type of transfer learning, has fewer restrictions on the choice of auxiliary data. It has shown promising performance in visual learning. However, existing self-taught learning methods usually ignore the structure information in data. In this paper, we focus on building a self-taught coding framework, which can effectively utilize the rich low-level pattern information abstracted from the auxiliary domain, in order to characterize the high-level structural information in the target domain. By leveraging a high quality dictionary learned across auxiliary and target domains, the proposed approach learns expressive codings for the samples in the target domain. Since many types of visual data have been proven to contain subspace structures, a low-rank constraint is introduced into the coding objective to better characterize the structure of the given target set. The proposed representation learning framework is called self-taught low-rank (S-low) coding, which can be formulated as a nonconvex rank-minimization and dictionary learning problem. We devise an efficient majorization-minimization augmented Lagrange multiplier algorithm to solve it. Based on the proposed S-low coding mechanism, both unsupervised and supervised visual learning algorithms are derived. Extensive experiments on five benchmark data sets demonstrate the effectiveness of our approach.

关键词： Data clustering image classification low-rank coding self-taught learning (STL) transfer learning

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Deep Transfer low-rank coding for Cross-Domain Learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019年第6期30卷 1768-1779页

作者： Ding, Zhengming Fu, Yun Indiana Univ Purdue Univ Indianapolis Dept Comp Informat & Technol Indianapolis IN 46202 USA Northeastern Univ Dept Elect & Comp Engn Coll Comp & Informat Sci Boston MA 02115 USA

Transfer learning has attracted great attention to facilitate the sparsely labeled or unlabeled target learning by leveraging previously well-established source domain through knowledge transfer. Recent activities on transfer learning attempt to build deep architectures to better tight off cross-domain divergences by extracting more effective features. However, its generalizability would decrease greatly due to the domain mismatch enlarges, particularly at the top layers. In this paper, we develop a novel deep transfer low-rank coding based on deep convolutional neural networks, where we investigate multilayer low-rank coding at the top task-specific layers. Specifically, multilayer common dictionaries shared across two domains are obtained to bridge the domain gap such that more enriched domain-invariant knowledge can be captured through a layerwise fashion. With rank minimization on the new codings, our model manages to preserve the global structures across source and target, and thus, similar samples of two domains tend to gather together for effective knowledge transfer. Furthermore, domain/classwise adaption terms are integrated to guide the effective coding optimization in a semisupervised manner, so the marginal and conditional disparities of two domains will be alleviated. Experimental results on three visual domain adaptation benchmarks verify the effectiveness of our proposed approach on boosting the recognition performance for the target domain, by comparing it with other state-of-the-art deep transfer learning.

关键词： Deep learning low-rank coding transfer learning

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Fast Locality-constrained low-rank coding for Image Classification

Fast Locality-constrained Low-rank Coding for Image Classifi...

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Chinese Automation Congress (CAC)

作者： Fan Min Wang Fen Wang Kai Shi Xin Liu Zhihong Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Elect State Co Chongqing 400044 Peoples R China

ISBN: (纸本)9781467371896

In this paper, we propose a Fast Locality-constrained low-rank sparse coding for image classification. The low-rank coding seeks the homogeneousness and correlation of local features, encodes jointly and globally, based on the traditional low-rank coding, we incorporate locality constraints to enforce the local features sharing the same representation. Considering that the traditional low-rank coding optimization algorithms have the same complexity with the sparse coding, which is difficult to scaly used to classification. We propose a fast low-rank optimization approach, it replaces the nuclear norm with the Frobenius norm, which not only has a closed form solution but sort the class labels very fast. Experiments show that locality works better than sparse at exploiting the local neighbor correlations, low-rank coding shows advantages of finding spatial layout correlations of local features, encoding jointly and globally. It has standout classification performance which has 2% improvement compared to the standard methods. Moreover, our method has great computation efficiency, the coding time is a bit more than LLC, and it is only 19.40%similar to 21.18% of that of ScSPM, the classification time is also the least.

关键词： image classification low-rank coding locality-constraint fast

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NetSRE: Link predictability measuring and regulating

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KNOWLEDGE-BASED SYSTEMS 2020年 196卷 105800-105800页

作者： Xian, Xingping Wu, Tao Qiao, Shaojie Wang, Xi-Zhao Wang, Wei Liu, Yanbing Chongqing Univ Posts & Telecommun Dept Comp Sci & Technol Chongqing Peoples R China Chongqing Univ Posts & Telecommun Sch Cybersecur & Informat Law Chongqing Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Sichuan Univ Inst Cybersecur Chengdu Peoples R China Chongqing Univ Posts & Telecommun Chongqing Engn Lab Internet & Informat Secur Chongqing Peoples R China

Link prediction is an elemental issue for network-structured data mining, which has already found a wide range of applications. The organization of real-world networks usually embodies both regularities and irregularities, and the precision of link prediction algorithms coincides with the portion of a network being categorized as regular. Quantifying and controlling how well an unobserved link can be predicted is a fundamental problem in link prediction. This paper proposes a structural regularity-exploring architecture, called NetSRE, for measuring and regulating link predictability of networks. The proposed NetSRE assumes that there are consistent interaction patterns across the local subgraphs of networks and one of them can be represented by a linear summation of the others, and thus, link predictability can be characterized by the self-representation degree of network structures. Specifically, NetSRE includes (1) a low Frobenius norm pursuit-based self-representation network model for predicting the "true" underlying networks, (2) a "structural regularity" index for measuring the link predictability of networks, i.e., the inherent difficulty of link prediction independent of specific algorithms, and (3) an importance measuring method for structural role exploration of network links and a link-based structure perturbation algorithm for link predictability regulation. Experimental results on real-world networks validate the performance of our method. It is found that real-world networks have various structural regularities and link predictability can be estimated based on structure mining directly. We show that network heterogeneity provides a way to intrinsically segregate network links into qualitatively distinct groups, which have different influences on the link predictability of networks. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Network data Link prediction Link predictability Structural patterns low-rank coding Structure perturbation

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