Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

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

doi:10.1007/978-3-319-46672-9_49

Cited by 30 publications

(29 citation statements)

References 11 publications

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“…(3) H * is referring to the common latent factor of all views. We use a single parameter γ to control the distribution of weight factors α (v) in all V views, such that the important views will be assigned bigger weights [14]. Also, in order to make different [7] to simplify the computation.…”

Section: The Objective Function Of Clflmentioning

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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Section: The Objective Function Of Clflmentioning

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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“…Recently, Shao et al [37] proposed a online multi-view clustering method with incomplete view via imposing lasso regularization on the representation of each view. More references can be referred to [16,33,40,50]. These methods are useful for the nonnegative multiview data analysis, however, they are not suitable for the noisy views and incomplete views, which are often encountered in real applications.…”

Section: Multi-viewmentioning

confidence: 99%

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Long

Tan

et al. 2017

Multimed Tools Appl

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With the increasing availability of multiview nonnegative data in real applications, multiview representation learning based on nonnegative matrix factorization (NMF) has attracted more and more attentions. However, existing NMF-based methods are sensitive to noises and are difficult to generate discriminative features with noisy views. To address these problems, we propose a co-regularized multiview nonnegative matrix factorization method with correlation constraint for nonnegative representation learning, which jointly exploits consistent and complementary information across different views. Different from previous works, we aim at integrating information from multiple views efficiently and making it more robust to the presence of noisy views. More specifically, we exploit the complementary information of multiple views through the co-regularization to accommodate the presence of the noisy views. Meanwhile, correlation constraint is imposed on the low-dimensional space to learn a common latent representation shared by different views. For the induced objective function, we derive an alternative algorithm to solve the optimization problem. The experimental results on four real datasets demonstrate the effectiveness and robustness of the proposed algorithm.

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“…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%

“…Wang et al [24] extended MultiNMF to semi-supervised setting by ensuring that data with same label have same representations and use a single parameter to learn the weight of each view adaptively.…”

Section: Introductionmentioning

confidence: 99%

Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Wang

Tian

et al. 2018

IEEE Trans. Cybern.

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126

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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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Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

Cited by 30 publications

References 11 publications

NMF-Based Comprehensive Latent Factor Learning with Multiview Da

NMF-Based Comprehensive Latent Factor Learning with Multiview Da

Co-regularized multiview nonnegative matrix factorization with correlation constraint for representation learning

Diverse Non-Negative Matrix Factorization for Multiview Data Representation

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