Multi-view Clustering via Multi-manifold Regularized Nonnegative Matrix Factorization

Zhang, Xianchao; Zhao, Long; Zong, Linlin; Liu, Xinyue; Yu, Hong

doi:10.1109/icdm.2014.19

Cited by 40 publications

(24 citation statements)

References 14 publications

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“…One of the most commonly used approaches to define the weight matrix A (v) on the graph is 0 − 1 weighting [8] . If x [21], we adopt this approach for it is simple to implement and performs well in practice. Combining this locality preserved regularizer with the objective function of DiNMF (9) gives rise to our LPDiNMF, which minimizes the objective function as follows:…”

Section: A Objective Function Of Lp-dinmf Methodsmentioning

confidence: 99%

“…• MMNMF [21]: It preserves the locally geometrical structure of the manifolds for multi-view clustering with regarding that the intrinsic manifold of the dataset is embedded in a convex hull of all the views' manifolds, and incorporates such an intrinsic manifold and an intrinsic coefficient matrix with a multi-manifold regularizer.…”

Section: B Methods To Comparementioning

confidence: 99%

“…MultiNMF [20] formulated a joint multi-view NMF learning process with the constraint that encourages representation of each view towards a common consensus. Subsequently, several approaches [21], [22], [23], [24] were proposed based on MultiNMF. Specifically, Zhang et al [21] developed a multi-manifold NMF (MMNMF) by incorporating the locally geometrical structure of data across multiple views.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, several approaches [21], [22], [23], [24] were proposed based on MultiNMF. Specifically, Zhang et al [21] developed a multi-manifold NMF (MMNMF) by incorporating the locally geometrical structure of data across multiple views. It regards each view as one manifold and the intrinsic manifold of a dataset as a mixture of the manifolds.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Wang

Tian

et al. 2018

IEEE Trans. Cybern.

135

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Abstract-Nonnegative matrix factorization (NMF), a method for finding parts-based representation of nonnegative data, has shown remarkable competitiveness in data analysis. Given that real-world datasets are often comprised of multiple features or views which describe data from various perspectives, it is important to exploit diversity from multiple views for comprehensive and accurate data representations. Moreover, real-world datasets often come with high-dimensional features, which demands the efficiency of low-dimensional representation learning approaches. To address these needs, we propose a Diverse Nonnegative Matrix Factorization (DiNMF) approach. It enhances the diversity, reduces the redundancy among multi-view representations with a novel defined diversity term and enables the learning process in linear execution time. We further propose a Locality Preserved DiNMF (LP-DiNMF) for more accurate learning, which ensures diversity from multiple views while preserving the local geometry structure of data in each view. Efficient iterative updating algorithms are derived for both DiNMF and LP-DiNMF, along with proofs of convergence. Experiments on synthetic and realworld datasets have demonstrated the efficiency and accuracy of the proposed methods against the state-of-the-art approaches, proving the advantages of incorporating the proposed diversity term into NMF.

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Section: A Objective Function Of Lp-dinmf Methodsmentioning

confidence: 99%

Section: B Methods To Comparementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Wang

Tian

et al. 2018

IEEE Trans. Cybern.

135

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show abstract

“…Subsequently, Wang et al [15] proposed a regression-like objective, which conducts multi-view clustering and feature selection at the same time. Zhang et al [16] also developed MMNMF which attempted to preserve intrinsic geometrical structure of data across multiple views. Cao et al [5] utilized a diversity constraints on subspaces to enhance the complementarity among multiple views.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Wang

Tian

et al. 2016

Neural Information Processing

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Abstract. Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using L2,1-norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.

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Multi-view Clustering with Complete Information

Ding

Zhao

2018

Learning Representation for Multi-View Data Analysis

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Multi-view Clustering via Multi-manifold Regularized Nonnegative Matrix Factorization

Cited by 40 publications

References 14 publications

Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Multi-view Clustering with Complete Information

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