IEEE Transactions on Circuits and Systems for Video Technology· 2021Q1
Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering
- 201citations
- Q1SCImago
- 2021year
Short summary
A novel multi-view clustering method, Low-Rank Tensor Graph (LRTG), simultaneously learns data representation and affinity matrices in a single step, outperforming 17 state-of-the-art methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes Low-Rank Tensor Graph (LRTG) for multi-view clustering.
- Simultaneously learns representation and affinity matrix in one step.
- Uses Tucker decomposition and L2,1-norm to handle noise and outliers.
- Includes an adaptive neighbor scheme for flexible graph construction.
- Demonstrates superior performance against 17 existing methods in experiments.
AI-generated from the title and abstract; the full text is not read.
Abstract
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 <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> ow- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</u> ank <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ensor <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> 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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <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 xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <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.
The authors' abstract, as published at the source. IEEE Transactions on Circuits and Systems for Video Technology, 2021 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics