Adaptively Topological Tensor Network for Multi-view Subspace Clustering

TL;DR

This paper introduces an adaptive tensor network approach for multi-view subspace clustering that dynamically determines tensor structure to better capture dataset-specific low-rank information, improving clustering performance.

Contribution

The paper proposes the adaptively topological tensor network (ATTN), which adaptively determines tensor structure based on data, enhancing multi-view clustering effectiveness over fixed tensor decompositions.

Findings

Outperforms existing methods on six multi-view datasets.

Effectively captures essential clustering information with adaptive tensor structures.

Improves low-rank representation by pruning weakly correlated links.

Abstract

Multi-view subspace clustering methods have employed learned self-representation tensors from different tensor decompositions to exploit low rank information. However, the data structures embedded with self-representation tensors may vary in different multi-view datasets. Therefore, a pre-defined tensor decomposition may not fully exploit low rank information for a certain dataset, resulting in sub-optimal multi-view clustering performance. To alleviate the aforementioned limitations, we propose the adaptively topological tensor network (ATTN) by determining the edge ranks from the structural information of the self-representation tensor, and it can give a better tensor representation with the data-driven strategy. Specifically, in multi-view tensor clustering, we analyze the higher-order correlations among different modes of a self-representation tensor, and prune the links of the weakly correlated ones from a fully connected tensor network. Therefore, the newly obtained tensor networks can efficiently explore the essential clustering information with self-representation with different tensor structures for various datasets. A greedy adaptive rank-increasing strategy is further applied to improve the capture capacity of low rank structure. We apply ATTN on multi-view subspace clustering and utilize the alternating direction method of multipliers to solve it. Experimental results show that multi-view subspace clustering based on ATTN outperforms the counterparts on six multi-view datasets.