Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Wang, Jing; Tian, Feng; Yu, Hongnian; Liu, Chang Hong; Zhan, Kun; Wang, Xiao

doi:10.1109/tcyb.2017.2747400

Cited by 126 publications

(33 citation statements)

References 35 publications

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“…The proposed SLSNMF is currently a single-view learning method. Due to the availability of multi-view data, it is natural and capable of extending the proposed SLSNMF method to a multi-view learning one [37]- [40]. Therefore, we will consider this as part of our future work.…”

Section: Discussionmentioning

confidence: 99%

Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering

et al. 2019

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As a typical variation of nonnegative matrix factorization (NMF), symmetric NMF (SNMF) is capable of exploiting information of the cluster embedded in the matrix of similarity. The traditional SNMF-based methods for clustering first performs the techniques of the similarity learning on input data to learn a matrix of similarity, which is subsequently factorized by SNMF or one of its variants to learn information from the cluster. While these methods have led to satisfactory clustering results, it is suboptimal, since they do not explicitly exploit the fact that processes of the similarity learning and the clustering are depend on each other. In this paper, we describe a new SNMF model, termed similarity learning-induced SNMF (SLSNMF). SLSNMF can be considered as a unified framework that jointly considers these two processes. SLSNMF improves the clustering performance of SNMF by thoroughly exploring the mutual reinforcement between the process of similarity learning and the process of clustering until convergence. We incorporate a constraint into the standard SNMF model to learn the matrices of similarity and cluster simultaneously. Meanwhile, for solving this new model, we use the strategy of alternating iterative and derive an efficient algorithm, whose convergence is theoretically guaranteed. Experimental results over three benchmark image data sets demonstrate that SLSNMF outperforms the state-of-the-art methods for clustering. INDEX TERMS Symmetric nonnegative matrix factorization (SNMF), nonnegative matrix factorization (NMF), clustering, unsupervised learning.

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Section: Discussionmentioning

confidence: 99%

Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering

et al. 2019

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“…For example, MulNMF designed a constraint that encourages representation of each view toward a common consensus H * . DiNMF [23] introduced a constraint term tr(H (v) H (s)T ) to guarantee the diversity among points in different views. In order to deal with mixed-sign data, based on the semi-NMF model, a deep semi-NMF method couples the output representation in the final layer of factorization and enforces views that share the same representation after layer by layer factorization.…”

Section: Related Workmentioning

confidence: 99%

Multiview Clustering via Robust Neighboring Constraint Nonnegative Matrix Factorization

Chen

Wang

Pan

2019

Mathematical Problems in Engineering

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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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“…The authors in [9] took the local geometric structure of each view into consideration, and penalized the disagreement of different views at the same time. And Wang et al [10] proposed a diverse NMF algorithm which adds a diversity constraint to ensure that data vectors from different views be as diverse as possible, by regularizing the dot product of two vectors from different views close to zero.…”

Section: Introductionmentioning

confidence: 99%

“…All the methods above only focus on one type of perspective among multiple views. For example, Liu [7] focuses on the consistency while Wang [10] focuses on the complementarity. The consistency aims to maximize the agreement among the multiple views, while the complementarity states that each view may contain some knowledge that other views do not have [1].…”

Section: Introductionmentioning

confidence: 99%

NMF-Based Comprehensive Latent Factor Learning with Multiview Da

Zheng

Liang

Tian

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Multiview representations reveal the latent information of the data from different perspectives, consistency and complementarity. Unlike most multiview learning approaches, which focus only one perspective, in this paper, we propose a novel unsupervised multiview learning algorithm, called comprehensive latent factor learning (CLFL), which jointly exploits both consistent and complementary information among multiple views. CLFL adopts a non-negative matrix factorization based formulation to learn the latent factors. It learns the weights of different views automatically which makes the representation more accurate. Experiment results on a synthetic and several real datasets demonstrate the effectiveness of our approach.

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Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Cited by 126 publications

References 35 publications

Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering

Similarity Learning-Induced Symmetric Nonnegative Matrix Factorization for Image Clustering

Multiview Clustering via Robust Neighboring Constraint Nonnegative Matrix Factorization

NMF-Based Comprehensive Latent Factor Learning with Multiview Da

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