Nonredundancy regularization based nonnegative matrix factorization with manifold learning for multiview data representation

Author(s):  
Guosheng Cui ◽  
Ye Li
2019 ◽  
Vol 164 ◽  
pp. 29-37 ◽  
Author(s):  
Shudong Huang ◽  
Peng Zhao ◽  
Yazhou Ren ◽  
Tianrui Li ◽  
Zenglin Xu

Author(s):  
Siyuan Peng ◽  
Zhijing Yang ◽  
Bingo Wing-Kuen Ling ◽  
Badong Chen ◽  
Zhiping Lin

2021 ◽  
Vol 19 (2) ◽  
pp. 2147-2178
Author(s):  
Qing Yang ◽  
◽  
Jun Chen ◽  
Najla Al-Nabhan ◽  

<abstract> <p>As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (RCNMF), which learns robust discriminative representations by leveraging the power of both L2, 1-norm NMF and constraint propagation. Specifically, RCNMF explicitly exploits global and local structures of data to make latent representations of instances involved by the same class closer and those of instances involved by different classes farther. Furthermore, RCNMF introduces the L2, 1-norm cost function for addressing the problems of noise and outliers. Moreover, L2, 1-norm constraints on the factorial matrix are used to ensure the new representation sparse in rows. Finally, we exploit an optimization algorithm to solve the proposed framework. The convergence of such an optimization algorithm has been proven theoretically and empirically. Empirical experiments show that the proposed RCNMF is superior to other state-of-the-art algorithms.</p> </abstract>


2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Feiqiong Chen ◽  
Guopeng Li ◽  
Shuaihui Wang ◽  
Zhisong Pan

Many real-world datasets are described by multiple views, which can provide complementary information to each other. Synthesizing multiview features for data representation can lead to more comprehensive data description for clustering task. However, it is often difficult to preserve the locally real structure in each view and reconcile the noises and outliers among views. In this paper, instead of seeking for the common representation among views, a novel robust neighboring constraint nonnegative matrix factorization (rNNMF) is proposed to learn the neighbor structure representation in each view, and L2,1-norm-based loss function is designed to improve its robustness against noises and outliers. Then, a final comprehensive representation of data was integrated with those representations of multiviews. Finally, a neighboring similarity graph was learned and the graph cut method was used to partition data into its underlying clusters. Experimental results on several real-world datasets have shown that our model achieves more accurate performance in multiview clustering compared to existing state-of-the-art methods.


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