Fast Disentangled Slim Tensor Learning for Multi-view Clustering

TL;DR

This paper introduces DSTL, a scalable and efficient tensor learning method for multi-view clustering that disentangles semantic-related and unrelated features to improve clustering quality and handle large-scale data.

Contribution

The paper proposes a novel disentangled tensor learning approach that directly models high-order correlations and reduces feature redundancy in multi-view clustering.

Findings

DSTL outperforms state-of-the-art methods in clustering accuracy.

The approach is computationally efficient for large-scale data.

Feature disentanglement improves clustering robustness.

Abstract

Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalable to large-scale data. (2) Although some methods address it by introducing bipartite graphs, they may result in sub-optimal solutions caused by an unstable anchor selection process. (3) They generally ignore the negative impact of latent semantic-unrelated information in each view. To tackle these issues, we propose a new approach termed fast Disentangled Slim Tensor Learning (DSTL) for multi-view clustering . Instead of focusing on the multi-view graph structures, DSTL directly explores the high-order correlations among multi-view latent semantic representations based on matrix factorization. To alleviate the negative influence of feature redundancy, inspired by robust PCA, DSTL disentangles the latent low-dimensional representation into a semantic-unrelated part and a semantic-related part for each view. Subsequently, two slim tensors are constructed with tensor-based regularization. To further enhance the quality of feature disentanglement, the semantic-related representations are aligned across views through a consensus alignment indicator. Our proposed model is computationally efficient and can be solved effectively. Extensive experiments demonstrate the superiority and efficiency of DSTL over state-of-the-art approaches. The code of DSTL is available at https://github.com/dengxu-nju/DSTL.