Efficient and Effective Regularized Incomplete Multi-view Clustering

Liu, Xinwang; Li, Miaomiao; Tang, Chang; Xia, Jingyuan; Xiong, Jian; Liu, Li; Kloft, Marius; Zhu, En

doi:10.1109/tpami.2020.2974828

Cited by 138 publications

(79 citation statements)

References 21 publications

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“…We conduct experiments on fifteen well-known popular datasets: 3-Sources, 2 20 New Groups (20-NGs), 3 100 Leaves (100-Ls), 4 BBC with 3 views (BBC (3v)), 5 BBC with 4 views (BBC (4v)), 6 BBCSport with 2 views (BS (2v)), 7 BBCSport with 4 views (BS (4v)), 8 BUAA [55], Coil [56], Digit, 9 NUS [57], ORL [58], Outdoor Scene (Scene) [59], Yale, 10 and Extended YaleB (YaleB) [60].…”

Section: A Datasetsmentioning

confidence: 99%

V$^3$H: View Variation and View Heredity for Incomplete Multiview Clustering

Fang

Zhou

et al. 2020

IEEE Trans. Artif. Intell.

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Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V 3 H). Inspired by the variation and the heredity in genetics, V 3 H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V 3 H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V 3 H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V 3 H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts.Impact Statement-Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V 3 H fully learns the information of each view. Compared with the state-of-the-art algorithms, V 3 H improves clustering performance by more than 20% in representative cases. With the large improvement on multiple datasets, V 3 H has wide potential applications including the analysis of pandemic, financial and election datasets. The DOI of our codes is 10.24 433/CO.2 119 636.v1.

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Section: A Datasetsmentioning

confidence: 99%

V$^3$H: View Variation and View Heredity for Incomplete Multiview Clustering

Fang

Zhou

et al. 2020

IEEE Trans. Artif. Intell.

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“…Clustering has been intensively studied by combing information to categorize unlabelled data items into appropriate groups 1‐16 . With increasing data collection, many data in the real‐world could be presented from different perspectives, which are termed multiview data in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Followed the multiview SNMF methods, NMF‐based IMVC methods consider the incomplete data items with zero weight and therefore can be regarded as a special weighted version for NMF MVC. Most of them take the strategy of combining the view‐specific and common representations into a unified one 7‐10,17‐28 . They usually accomplish the missing features with mean values, and then use the weighted NMF to reduce the weight of the missing samples to obtain a consistent representation 29 .…”

Section: Introductionmentioning

confidence: 99%

Partial multiview clustering with locality graph regularization

Lian

Wang

et al. 2021

Int J Intell Syst

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Multiview clustering (MVC) collects complementary and abundant information, which draws much attention in machine learning and data mining community. Existing MVC methods usually hold the assumption that all the views are complete. However, multiple source data are often incomplete in real‐world applications, and so on sensor failure or unfinished collection process, which gives rise to incomplete multiview clustering (IMVC). Although enormous efforts have been devoted in IMVC, there still are some urgent issues that need to be solved: (i) The locality among multiple views has not been utilized in the existing mechanism; (ii) Existing methods inappropriately force all the views to share consensus representation while ignoring specific structures. In this paper, we propose a novel method termed partial MVC with locality graph regularization to address these issues. First, followed the traditional IMVC approaches, we construct weighted semi‐nonnegative matrix factorization models to handle incomplete multiview data. Then, upon the consensus representation matrix, the locality graph is constructed for regularizing the shared feature matrix. Moreover, we add the coefficient regression term to constraint the various base matrices among views. We incorporate the three aforementioned processes into a unified framework, whereas they can negotiate with each other serving for learning tasks. An effective iterative algorithm is proposed to solve the resultant optimization problem with theoretically guaranteed convergence. The comprehensive experiment results on several benchmarks demonstrate the effectiveness of the proposed method.

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“…Although the algorithms mentioned above have achieved great success in different scenarios, these traditional multi-view clustering algorithms cannot effectively deal with multi-view data with incomplete features. Therefore, the incomplete multi-view clustering algorithms [ 32 , 33 , 34 ] have attracted extensive attention. To the best of our knowledge, existing incomplete multi-view clustering algorithms can be classified into two categories: non-negative matrix factorization based methods and graph-based methods.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

Zhang

Wang

et al. 2020

Sensors

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With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method.

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Efficient and Effective Regularized Incomplete Multi-view Clustering

Cited by 138 publications

References 21 publications

V$^3$H: View Variation and View Heredity for Incomplete Multiview Clustering

V$^3$H: View Variation and View Heredity for Incomplete Multiview Clustering

Partial multiview clustering with locality graph regularization

Adaptive Weighted Graph Fusion Incomplete Multi-View Subspace Clustering

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