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Sparse dual graph-regularized deep nonnegative matrix factorization for image clustering

Weiyu Guo
2021 IEEE Access  
INDEX TERMS Deep nonnegative matrix factorization, dual graph regularization, sparse constraints, image clustering. 39926 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  Deep nonnegative matrix factorization (Deep NMF) as an emerging technique for image clustering has attracted more and more attention.  ...  CONCLUSION In this paper, we propose a novel image clustering approach, referred to as Sparse Dual Graph-regularized Deep Nonnegative Matrix Factorization (SDG Deep NMF), to address some challenges in  ... 
doi:10.1109/access.2021.3064631 fatcat:gfui7rogzbffrfpls23nmsqx3y

A Survey on Concept Factorization: From Shallow to Deep Representation Learning [article]

Zhao Zhang, Yan Zhang, Mingliang Xu, Li Zhang, Yi Yang, Shuicheng Yan
2021 arXiv   pre-print
As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade.  ...  Specifically, we first re-view the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases.  ...  ACKNOWLEDGMENT This work is partially supported by the National Natural Science Foundation of China (61672365) and the Fundamental Research Funds for the Central Universities of China (JZ2019H-  ... 
arXiv:2007.15840v3 fatcat:ahun2mogmfapxe4mqhqlsakyku

Deep Self-representative Concept Factorization Network for Representation Learning [article]

Yan Zhang, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha, Meng Wang
2019 arXiv   pre-print
DSCF-Net also improves the robustness by subspace recovery for sparse error correction firstly and then performs the deep factorization in the recovered visual subspace.  ...  clustering deep features.  ...  LCCF) [4] , Self-Representative Manifold Concept Factorization (SRMCF) [5] , and some dual-graph regularized methods, e.g., Dual Regularization NMF (DNMF) [6] and Dual-graph regularized CF (GCF) [  ... 
arXiv:1912.06444v4 fatcat:hasuiek27zhbvaoqhyvxp7wj5u

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep  ...  Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  Nonnegative Deep autoencoder-like nonnegative matrix factorization for DANMF Matrix Fatorization [131] community detection Density-Based Spatial Clustering of A density-based algorithm for discovering  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

Constrained Dual Graph Regularized Orthogonal Nonnegative Matrix Tri-Factorization for Co-Clustering

Shaodi Ge, Hongjun Li, Liuhong Luo
2019 Mathematical Problems in Engineering  
In this paper, we propose a constrained dual graph regularized orthogonal nonnegative matrix trifactorization (CDONMTF) algorithm to solve the coclustering problems.  ...  Clustering experiments on 5 UCI machine-learning data sets and 7 image benchmark data sets show that the achievement of the proposed algorithm is superior to that of some existing clustering algorithms  ...  [36] proposed a dual-graph regularized NMF with sparse and orthogonal constraints (SODNMF) and obtained encouraging clustering results.  ... 
doi:10.1155/2019/7565640 fatcat:pgf346pzdnbgrnagw5utpoj3zu

Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior [article]

Yan Zhang, Zhao Zhang, Yang Wang, Zheng Zhang, Li Zhang, Shuicheng Yan, Meng Wang
2021 arXiv   pre-print
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation  ...  In this paper, we technically propose a new enriched prior based Dual-constrained Deep Semi-Supervised Coupled Factorization Network, called DS2CF-Net, for learning the hierarchical coupled representations  ...  Dual-graph regularized CF (GCF) [13] .  ... 
arXiv:2009.03714v2 fatcat:fwa2ojfpyjdcrmcm3oidf5v44a

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review [article]

Xin-Ru Feng, Heng-Chao Li, Rui Wang, Qian Du, Xiuping Jia, Antonio Plaza
2022 arXiv   pre-print
Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem.  ...  In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing.  ...  Among them, semi-nonnegative matrix factorization (semi-NMF) was used for the optimization to process an entire image in the matrix form [61] , [135] . B.  ... 
arXiv:2205.09933v1 fatcat:77udhvg55fdftidm554qwarqzy

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Shicheng Li, Shumin Lai, Yan Jiang, Wenle Wang, Yugen Yi, Henry Man Fai Leung
2021 Computational Intelligence and Neuroscience  
To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work.  ...  Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR.  ...  Matrix factorization (MF) is a brilliant framework for FR, which has been widely used for anomaly detection such as principal component analysis (PCA) [17] and nonnegative matrix factorization (NMF)  ... 
doi:10.1155/2021/4026132 pmid:34777492 pmcid:PMC8580626 fatcat:5hkku6kzb5aazaeydsjpniyezi

A Graph Regularized Deep Neural Network for Unsupervised Image Representation Learning

Shijie Yang, Liang Li, Shuhui Wang, Weigang Zhang, Qingming Huang
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
From the perspective of manifold learning, we propose a graph regularized deep neural network (GR-DNN) to endue traditional DAEs with the ability of retaining local geometric structure.  ...  Theoretical analysis presents the close relationship between the proposed graph regularizer and the graph Laplacian regularizer in terms of the optimization objective.  ...  Comparison Methods (1) KMEANS: K-means clustering on raw inputs. (2) N-MF: Nonnegative matrix factorization [12] . (3) GNM-F: Graph-regularized nonnegative matrix factorization [4] .  ... 
doi:10.1109/cvpr.2017.746 dblp:conf/cvpr/YangLWZH17 fatcat:xdf6ah4trzh7pomd73kpytfm5a

Integrated Sparse Coding With Graph Learning for Robust Data Representation

Yupei Zhang, Shuhui Liu
2020 IEEE Access  
INDEX TERMS Graph sparse coding, graph embedding, data structure preserving, robust data representation, image representation, one-step integrated graph sparse coding.  ...  The proposed method is dubbed low-rank graph regularized sparse coding (LogSC), which learns sparse codes and low-rank representations jointly rather than the traditional two-step approach.  ...  Besides, the proposed strategy could be extended for graph regularized nonnegative matrix factorization [35] and tensor decomposition [36] .  ... 
doi:10.1109/access.2020.3021081 fatcat:ebffu32jinckllyhfua2w3ezmm

2019 Index IEEE Transactions on Knowledge and Data Engineering Vol. 31

2020 IEEE Transactions on Knowledge and Data Engineering  
Liu, J., +, TKDE July 2019 1397-1411 Clustering algorithms Community Detection in Multi-Layer Networks Using Joint Nonnegative Matrix Factorization.  ...  Liu, H., +, TKDE Dec. 2019 2319-2331 Clustering methods Dual Hypergraph Regularized PCA for Biclustering of Tumor Gene Expres- sion Data.  ... 
doi:10.1109/tkde.2019.2953412 fatcat:jkmpnsjcf5a3bhhf4ian66mj5y

A Network-based Sparse and Multi-manifold Regularized Multiple Non-negative Matrix Factorization for Multi-View Clustering

Lihua Zhou, Guowang Du, Kevin Lü, Lizhen Wang
2021 Expert systems with applications  
In this paper, a network-based sparse and multi-manifold regularized multiple NMF (NSM_MNMF) for multi-view clustering is proposed, where multi-view data is transformed into multiple networks, and NMF  ...  is used to jointly factorize transformed multiple networks for capturing the shared cluster structure embedded in different views.  ...  Acknowledgments This work was supported by the National Natural Science Foundation of China (61762090, 62062066, and 61966036), the Program for Innovation Research Team (in Science and Technology) in University  ... 
doi:10.1016/j.eswa.2021.114783 fatcat:iwncj7urm5epldp7fnpbbd5kf4

Transductive Nonnegative Matrix Tri-Factorization

Xiao Teng, Long Lan, Xiang Zhang, Guohua Dong, Zhigang Luo
2020 IEEE Access  
INDEX TERMS Nonnegative matrix factorization, nonnegative matrix tri-factorization, transductive learning.  ...  Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices.  ...  Lin for her assistance with the experiment.  ... 
doi:10.1109/access.2020.2989527 fatcat:ux7lny42gjag5ftaa2tvmf42ay

New Approaches in Multi-View Clustering [chapter]

Fanghua Ye, Zitai Chen, Hui Qian, Rui Li, Chuan Chen, Zibin Zheng
2018 Recent Applications in Data Clustering  
learning versions, which include k-means, spectral clustering, matrix factorization, tensor decomposition, and deep learning.  ...  These clustering methods are the most widely employed algorithms for single-view data, and lots of efforts have been devoted to extending them for multi-view clustering.  ...  For instance, a pair-wise sparse subspace representation model for multi-view clustering proposed in [10] can be unified into the framework of matrix factorization.  ... 
doi:10.5772/intechopen.75598 fatcat:jniifuf4ync27fofz4fpbnfiia

Semi-supervised dual-dictionary learning for heterogeneous transfer learning on cross-scene hyperspectral images

Hong Chen, Minchao Ye, Ling Lei, Huijuan Lu, Yuntao Qian
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this article, we propose a heterogeneous transfer learning algorithm namely semisupervised dual-dictionary nonnegative matrix factorization (SS-DDNMF).  ...  SS-DDNMF consists of two contributions. 1) Dual-dictionary nonnegative matrix factorization (DDNMF): DDNMF trains two dictionaries for source and target scenes, respectively, aiming at projecting the source  ...  The research work in [24] combined three regularization terms for NMF: the smooth regularization on basis matrix, the sparse regularization on coefficient matrix, and the graph (manifold) regularization  ... 
doi:10.1109/jstars.2020.3000677 fatcat:phlqgawptfe53fv3oxtzajlnzm
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