Multi-view clustering by joint spectral embedding and spectral rotation

Wan, Zhizhen; Xu, Huiling; Gao, Quanxue

doi:10.1016/j.neucom.2021.07.090

Cited by 15 publications

(2 citation statements)

References 18 publications

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“…This framework directly obtains the discrete labels of data and achieves good performance, but it, despite working for single-view data, cannot be directly applied to multi-view data which are ubiquitous in artificial intelligence and pattern recognition. To get rid of this limitation, some works extended this framework to multi-view clustering [20][21][22]. However, they used minimum mean squared error, which is one-dimensional and a pixel-by-pixel measurement method, to learn the view-shared adjacency matrix.…”

Section: Related Workmentioning

confidence: 99%

“…This framework simultaneously optimizes spectral embedding and spectral rotation to achieve the discrete labels, but it, despite working for single-view data, cannot be directly applied to multi-view data, which are ubiquitous in artificial intelligence and pattern recognition. Based on this framework, some works extended it to multi-view clustering [20][21][22]. But all of them minimize the divergence between the adjacency matrices of views by minimum mean square error, which is a one-dimensional and pixel-by-pixel measurement model.…”

Section: Introductionmentioning

confidence: 99%

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Tensorized Discrete Multi-View Spectral Clustering

Li,

Yang,

Yun

et al. 2024

Electronics

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Discrete spectral clustering directly obtains the discrete labels of data, but existing clustering methods assume that the real-valued indicator matrices of different views are identical, which is unreasonable in practical applications. Moreover, they do not effectively exploit the spatial structure and complementary information embedded in views. To overcome this disadvantage, we propose a tensorized discrete multi-view spectral clustering model that integrates spectral embedding and spectral rotation into a unified framework. Specifically, we leverage the weighted tensor nuclear-norm regularizer on the third-order tensor, which consists of the real-valued indicator matrices of views, to exploit the complementary information embedded in the indicator matrices of different views. Furthermore, we present an adaptively weighted scheme that takes into account the relationship between views for clustering. Finally, discrete labels are obtained by spectral rotation. Experiments show the effectiveness of our proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tensorized Discrete Multi-View Spectral Clustering

Li,

Yang,

Yun

et al. 2024

Electronics

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A Unified Spectral Rotation Framework Using a Fused Similarity Graph

Liang,

Bai,

Jiang

2023

Lecture Notes in Computer Science

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Representation Learning in Multi-view Clustering: A Literature Review

Chen

Lin

et al. 2022

Data Sci. Eng.

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Multi-view clustering (MVC) has attracted more and more attention in the recent few years by making full use of complementary and consensus information between multiple views to cluster objects into different partitions. Although there have been two existing works for MVC survey, neither of them jointly takes the recent popular deep learning-based methods into consideration. Therefore, in this paper, we conduct a comprehensive survey of MVC from the perspective of representation learning. It covers a quantity of multi-view clustering methods including the deep learning-based models, providing a novel taxonomy of the MVC algorithms. Furthermore, the representation learning-based MVC methods can be mainly divided into two categories, i.e., shallow representation learning-based MVC and deep representation learning-based MVC, where the deep learning-based models are capable of handling more complex data structure as well as showing better expression. In the shallow category, according to the means of representation learning, we further split it into two groups, i.e., multi-view graph clustering and multi-view subspace clustering. To be more comprehensive, basic research materials of MVC are provided for readers, containing introductions of the commonly used multi-view datasets with the download link and the open source code library. In the end, some open problems are pointed out for further investigation and development.

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Multi-view clustering by joint spectral embedding and spectral rotation

Cited by 15 publications

References 18 publications

Tensorized Discrete Multi-View Spectral Clustering

Tensorized Discrete Multi-View Spectral Clustering

A Unified Spectral Rotation Framework Using a Fused Similarity Graph

Representation Learning in Multi-view Clustering: A Literature Review

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