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Semi-supervised graph regularized nonnegative matrix factorization with local coordinate for image representation

SIGNAL PROCESSING-IMAGE COMMUNICATION 2022年 102卷 116589-116589页

作者： Li, Huirong Gao, Yuelin Liu, Junmin Zhang, Jiangshe Li, Chao Shangluo Univ Sch Math & Comp Applicat Shangluo 726000 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China North Natl Univ Res Inst Syst Sci Yinchuan 750021 Ningxia Peoples R China

nonnegative matrix factorization(NMF) is a powerful image representation algorithm in pattern recognition and data mining. However, the traditional NMF does not utilize any label information, or fail to guarantee the sparse parts-based representation. In this paper, we put forward a semi-supervised local coordinate NMF (SLNMF) algorithm, which incorporate the available label information and the local coordinate constraint into NMF. Particularly, SLNMF makes the learned coefficients sparse by adding the coordinate constraint, and enhance the discriminative ability of different classes by using the label information constraint. Furthermore, in order to extract the geometric structure of the data space, we propose a new semi-supervised graph regularized NMF with local coordinate constraint (SGLNMF) method, which incorporates the graph regularization into SLNMF to enhance the discriminative abilities of data representations. SGLNMF not only reveals the intrinsic geometrical information of the data space, but also takes into account the local coordinate constraint and the label information. Clustering experiments on several standard image datasets demonstrate the effectiveness of our proposed SLNMF and SGLNMF methods compared to the state-of-the-art methods.

关键词： nonnegative matrix factorization Graph regularization Semi-supervised learning Local coordinate

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Community detection algorithm based on nonnegative matrix factorization and pairwise constraints

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PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2020年 545卷 123491-123491页

作者： Lu, Hong Sang, Xiaoshuang Zhao, Qinghua Lu, Jianfeng Nanjing Univ Sci & Technol Sch Comp Sci Xiaolingwei 200 Nanjing 210094 Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China

Community detection is a critical issue in the field of complex networks. nonnegative matrix factorization (NMF) has been one of the hot research topics in community detection. In the real world, only topology information is inadequate to detect community structure accurately. To utilize the limited supervised information effectively, we propose two algorithms for community detection, which combine NMF or (Symmetric NMF) with pairwise constraints to deal with linearly separable data and nonlinearly separable data, respectively. Currently, most of NMF-based community detection methods need the number of communities in advance or searching among all candidates with different K. To address this issue, we use singular value decomposition to reveal the number of communities automatically. Experimental results demonstrate that the proposed methods can produce higher clustering performance compared with some state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Community detection nonnegative matrix factorization Singular value decomposition Pairwise constraints Complex networks

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Joint feature representation and classification via adaptive graph semi-supervised nonnegative matrix factorization

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 89卷 115984-115984页

作者： Yi, Yugen Chen, Yuqi Wang, Jianzhong Lei, Gang Dai, Jiangyan Zhang, Huihui Jiangxi Normal Univ Sch Software Nanchang 330022 Jiangxi Peoples R China Northeast Normal Univ Coll Informat Sci & Technol Changchun 130024 Peoples R China Weifang Univ Sch Comp Engn Weifang 261061 Peoples R China

As an effective feature representation method, non-negative matrix factorization (NMF) cannot utilize the label information sufficiently, which makes it not be suitable for the classification task. In this paper, we propose a joint feature representation and classification framework named adaptive graph semi-supervised nonnegative matrix factorization (AGSSNMF). Firstly, to enhance the discriminative ability of feature representation and accomplish the classification task, a regression model with nonnegative matrix factorization (called as RNMF) is proposed, which exploits the relation between the label information and feature representation. Secondly, to overcome the drawback of insufficient labels, an adaptive graph-based label propagation (refereed as AGLP) model is established, which adopts a local constraint to reflect the local structure of data. Then, we integrate RNMF and AGLP into a unified framework for feature representation and classification. Finally, an iterative optimization algorithm is used to solve the objective function. Extensive experiments show that the proposed framework has excellent performance compared with some well-known methods.

关键词： Feature representation nonnegative matrix factorization Adaptive graph Label propagation Classification

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Robust nonnegative matrix factorization with structure regularization

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NEUROCOMPUTING 2020年 412卷 72-90页

作者： Huang, Qi Yin, Xuesong Chen, Songcan Wang, Yigang Chen, Bowen Zhejiang Univ Sci & Technol Sch Biol & Chem Engn Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Media & Design Hangzhou Peoples R China Nanjing Inst Technol Sch Comp Engn Nanjing Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Peoples R China

nonnegative matrix factorization (NMF) has attracted more and more attention due to its wide applications in computer vision, information retrieval, and machine learning. In contrast to the original NMF and its variants, this paper proposes a novel unsupervised learning framework, called robust structured nonnegative matrix factorization (RSNMF) which respects both global and local structures of the data space. Specifically, to learn a discriminative representation, RSNMF explores both the global structure via considering the data variance and the local structure via exploiting the data neighborhood. To well address the problem of noise and outliers, it imposes joint L-2,L-1-norm minimization on both the loss function of NMF and the regularization of the basis matrix. The geometric structure and the joint L-2,L-1-norm are formulated as an optimization model, which is solved by the proposed iterative algorithm. Finally, the convergence of RSNMF is analyzed theoretically and empirically. The experimental results on real-world data sets show the effectiveness of our proposed algorithm in comparison to state-of-the-art algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： nonnegative matrix factorization Global structure Manifold regularization Clustering

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Vein Recognition Algorithm Based on Transfer nonnegative matrix factorization

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IEEE ACCESS 2020年 8卷 101607-101615页

作者： Jia, Xu Sun, Fuming Chen, Dali Liaoning Univ Technol Sch Elect & Informat Engn Jinzhou 121001 Peoples R China Dalian Minzu Univ Sch Elect & Commun Engn Dalian 116600 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

Most of the existing vein recognition algorithms are only effective for specified datasets, and once replacing the vein image acquisition device, i.e., the properties of the collected vein images are changed, the performance of the algorithm will be degraded greatly. Therefore, a transfer nonnegative matrix factorization (NMF) based vein recognition algorithm is proposed, which makes vein features more universal. Its contributions are mainly reflected in the following two aspects: 1) The orthogonal constraint is imposed on the model to reduce the redundancy between feature bases and increase the difference between the features of different veins;2) The differences between the vein features in different datasets are reduced based on Maximum Mean Difference (MMD) constraint, i.e., the knowledge of the source dataset is transferred to the target dataset well, and the universality of vein features can be improved. Experimental results show that the proposed algorithm outperforms state of the art methods on two dorsal hand vein datasets and two finger vein datasets.

关键词： Veins Feature extraction Image recognition Linear programming Transforms Redundancy Target recognition Vein recognition nonnegative matrix factorization transfer learning maximum mean difference gradient descent

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nonnegative matrix factorization and metamorphic malware detection

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JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES 2019年第3期15卷 195-208页

作者： Ling, Yeong Tyng Sani, Nor Fazlida Mohd Abdullah, Mohd Taufik Hamid, Nor Asilah Wati Abdul Univ Putra Malaysia Fac Comp Sci & Informat Technol Serdang Selangor Malaysia

Metamorphic malware change their internal code structure by adopting code obfuscation technique while maintaining their malicious functionality during each infection. This causes change of their signature pattern across each infection and makes signature based detection particularly difficult. In this paper, through static analysis, we use similarity score from matrix factorization technique called nonnegative matrix factorization for detecting challenging metamorphic malware. We apply this technique using structural compression ratio and entropy features and compare our results with previous eigenvector-based techniques. Experimental results from three malware datasets show this is a promising technique as the accuracy detection is more than 95%.

关键词： Metamorphic malware nonnegative matrix factorization Dimension reduction Structural analysis

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Adaptive graph regularized nonnegative matrix factorization for data representation

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APPLIED INTELLIGENCE 2020年第2期50卷 438-447页

作者： Zhang, Lin Liu, Zhonghua Pu, Jiexin Song, Bin Henan Univ Sci & Technol Informat Engn Coll Luoyang Peoples R China

As a classical data representation method, nonnegative matrix factorization (NMF) can well capture the global structure information of the observed data, and it has been successfully applied in many fields. It is generally known that the local manifold structures will have a better effect than the global structures in image recognition and clustering. The local structure information can well be preserved by the neighbor graph in the manifold learning methods. The traditional neighbor graph constructed relies heavily on the original observed data. However, the original data usually contain a lot of noise and outliers in practical application, which results in obtaining inaccurate neighbor graph, and ultimately leads to performance degradation. How to get the ideal local structure information becomes more and more important. By combing the manifold learning into NMF, we propose an adaptive graph regularized nonnegative matrix factorization (AGNMF). In AGNMF, the neighbor graph is obtained by adaptive iteration. Both the global information and the local manifold can be well captured in AGNMF, and the better data representation can be obtained. A large number of experiments on different data sets show that our AGNMF has good clustering ability.

关键词： nonnegative matrix factorization Graph regularization Manifold learning Image clustering

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Integration of Imaging (epi)Genomics Data for the Study of Schizophrenia Using Group Sparse Joint nonnegative matrix factorization

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第5期17卷 1671-1681页

作者： Wang, Min Huang, Ting-Zhu Fang, Jian Calhoun, Vince D. Wang, Yu-Ping Univ Elect Sci & Technol China Res Ctr Image & Vis Comp Sch Math Sci Chengdu 611731 Sichuan Peoples R China Jiangxi Univ Finance & Econ Sch Informat Technol Nanchang 330013 Jiangxi Peoples R China Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA Univ New Mexico Mind Res Network Albuquerque NM 87131 USA

Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI, and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to identify multi-dimensional modules. We also incorporate group structure information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with several brain activities. At the end, we have applied the method to the analysis of real data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to discover genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.

关键词： Sparse matrices Biological system modeling DNA Functional magnetic resonance imaging Correlation Biomarkers nonnegative matrix factorization group sparsity SNP fMRI methylation feature selection

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Regularized nonnegative matrix factorization with adaptive local structure learning

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NEUROCOMPUTING 2020年第0期382卷 196-209页

作者： Huang, Shudong Xu, Zenglin Kang, Zhao Ren, Yazhou Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China Peng Cheng Lab Ctr Artificial Intelligence Shenzhen 518055 Peoples R China

Due to the effectiveness of nonnegative matrix factorization (NMF) and its graph regularized extensions, these methods have been received much attention from various researchers. Generally, these methods are performed in two separate steps including Laplacian graph construction and the subsequent matrix ***, the similarity measurement for Laplacian graph is challenging since it's often affected by several factors such as the neighborhood size, choice of similarity metric, etc. As a result, the learned graph may be not suitable, let alone the subsequent matrix decomposition. In this paper, we propose adaptive graph regularized NMF. Different from existing methods, the similarity matrix is automatically learned from the data. The proposed model can simultaneously performs matrix decomposition and similarity learning. By balancing the interactions between both of the two subtasks in our model, each subtask is improved iteratively based on the result of another. Experimental results on benchmark data sets illustrate the effectiveness of our model. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Graph regularization Adaptive local structure learning nonnegative matrix factorization Clustering Similarity measure

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NMFGO: Gene Function Prediction via nonnegative matrix factorization with Gene Ontology

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第1期17卷 238-249页

作者： Yu, Guoxian Wang, Keyao Fu, Guangyuan Guo, Maozu Wang, Jun Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Xicheng Qu 100037 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China

Gene Ontology (GO) is a controlled vocabulary of terms that describe molecule function, biological roles, and cellular locations of gene products (i.e., proteins and RNAs), it hierarchically organizes more than 43,000 GO terms via the direct acyclic graph. A gene is generally annotated with several of these GO terms. Therefore, accurately predicting the association between genes and massive terms is a difficult challenge. To combat with this challenge, we propose an matrix factorization based approach called NMFGO. NMFGO stores the available GO annotations of genes in a gene-term association matrix and adopts an ontological structure based taxonomic similarity measure to capture the GO hierarchy. Next, it factorizes the association matrix into two low-rank matrices via nonnegative matrix factorization regularized with the GO hierarchy. After that, it employs a semantic similarity based k nearest neighbor classifier in the low-rank matrices approximated subspace to predict gene functions. Empirical study on three model species (S. cerevisiae, H. sapiens, and A. thaliana) shows that NMFGO is robust to the input parameters and achieves significantly better prediction performance than GIC, TO, dRW-kN, and NtN, which were re-implemented based on the instructions of the original papers. The supplementary file and demo codes of NMFGO are available at http://***/***?name=NMFGO.

关键词： Gene ontology gene function prediction nonnegative matrix factorization low-rank matrix

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