Cauchy loss induced block diagonal representation for robust multi-view subspace clustering

Yin, Ming; Liu, Wei; Li, Mingsuo; Jin, Taisong; Ji, Rongrong

doi:10.1016/j.neucom.2020.11.017

Cited by 20 publications

(2 citation statements)

References 26 publications

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“…In recent years, there have been numerous studies on multiview clustering, most of which implicitly or explicitly rely on the assumption of complete information. Based on this strong assumption, they can focus on extracting the shared semantics among the heterogeneous information across views in various ways [7], [9], [10], [11], [12], [13], [31], [32], [33], [34], [35]. However, in practice, this assumption may be violated, resulting in the problem of incomplete information, which can be two-fold: incomplete correspondences and incomplete instances.…”

Section: Multi-view Clusteringmentioning

confidence: 99%

Robust Multi-View Clustering With Incomplete Information

Yang

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

180

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Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples. The resulting consensus semantics remains unaffected by cross-view distribution shifts, making them useful for realigning/imputing defective instances and forming clusters. We demonstrate the effectiveness of SMILE through extensive comparison experiments with 13 state-of-the-art baselines on five benchmarks. Our approach improves the clustering accuracy of NoisyMNIST from 19.3%/23.2% to 82.7%/69.0% when the correspondences/instances are fully incomplete. We will release the code after acceptance.

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Section: Multi-view Clusteringmentioning

confidence: 99%

Robust Multi-View Clustering With Incomplete Information

Yang

et al. 2023

IEEE Trans. Pattern Anal. Mach. Intell.

180

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show abstract

“…In order to alleviate the impact of noise information on the clustering performance and make better use of the information of each view, scholars have proposed many methods. For example, Yin et al [40] used a more direct and intuitive block diagonal regularization to preserve the underlying structure of each view, and at the same time introduced the Cauchy loss function to deal with noise information. The underlying public structure of multi-view data can be effectively retained by the derived consistency representation matrix, and is robust to noise information and data damage.…”

Section: Introductionmentioning

confidence: 99%

Reweighted multi-view clustering with tissue-like P system

Chen

Liu

2023

PLoS ONE

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Multi-view clustering has received substantial research because of its ability to discover heterogeneous information in the data. The weight distribution of each view of data has always been difficult problem in multi-view clustering. In order to solve this problem and improve computational efficiency at the same time, in this paper, Reweighted multi-view clustering with tissue-like P system (RMVCP) algorithm is proposed. RMVCP performs a two-step operation on data. Firstly, each similarity matrix is constructed by self-representation method, and each view is fused to obtain a unified similarity matrix and the updated similarity matrix of each view. Subsequently, the updated similarity matrix of each view obtained in the first step is taken as the input, and then the view fusion operation is carried out to obtain the final similarity matrix. At the same time, Constrained Laplacian Rank (CLR) is applied to the final matrix, so that the clustering result is directly obtained without additional clustering steps. In addition, in order to improve the computational efficiency of the RMVCP algorithm, the algorithm is embedded in the framework of the tissue-like P system, and the computational efficiency can be improved through the computational parallelism of the tissue-like P system. Finally, experiments verify that the effectiveness of the RMVCP algorithm is better than existing state-of-the-art algorithms.

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K-BEST subspace clustering: kernel-friendly block-diagonal embedded and similarity-preserving transformed subspace clustering

Maggu,

Goel

2024

Pattern Anal Applic

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Cauchy loss induced block diagonal representation for robust multi-view subspace clustering

Cited by 20 publications

References 26 publications

Robust Multi-View Clustering With Incomplete Information

Robust Multi-View Clustering With Incomplete Information

Reweighted multi-view clustering with tissue-like P system

K-BEST subspace clustering: kernel-friendly block-diagonal embedded and similarity-preserving transformed subspace clustering

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