Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering

Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously dec...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology Vol. 32; no. 1; pp. 92 - 104
Main Authors: Chen, Yongyong, Xiao, Xiaolin, Peng, Chong, Lu, Guangming, Zhou, Yicong
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Affinity
Algorithms
Clustering
Clustering algorithms
Clustering methods
Correlation
graph learning
Learning
low-rank
Matrix decomposition
Multi-view clustering
Optimization
Outliers (statistics)
Representations
Sparse matrices
Subspace methods
Subspaces
tensor approximation
Tensors
ISSN:1051-8215, 1558-2205
Online Access:Get full text
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Summary:Graph and subspace clustering methods have become the mainstream of multi-view clustering due to their promising performance. However, (1) since graph clustering methods learn graphs directly from the raw data, when the raw data is distorted by noise and outliers, their performance may seriously decrease; (2) subspace clustering methods use a "two-step" strategy to learn the representation and affinity matrix independently, and thus may fail to explore their high correlation. To address these issues, we propose a novel multi-view clustering method via learning a L ow- R ank T ensor G raph (LRTG). Different from subspace clustering methods, LRTG simultaneously learns the representation and affinity matrix in a single step to preserve their correlation. We apply Tucker decomposition and <inline-formula> <tex-math notation="LaTeX">l_{2,1} </tex-math></inline-formula>-norm to the LRTG model to alleviate noise and outliers for learning a "clean" representation. LRTG then learns the affinity matrix from this "clean" representation. Additionally, an adaptive neighbor scheme is proposed to find the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> largest entries of the affinity matrix to form a flexible graph for clustering. An effective optimization algorithm is designed to solve the LRTG model based on the alternating direction method of multipliers. Extensive experiments on different clustering tasks demonstrate the effectiveness and superiority of LRTG over seventeen state-of-the-art clustering methods.
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2021.3055625