TPCH: Tensor-interacted Projection and Cooperative Hashing for Multi-view Clustering

TL;DR

TPCH introduces a tensor-based multi-view clustering method that captures higher-order interactions and improves clustering accuracy and efficiency on large-scale datasets.

Contribution

It proposes a novel tensor-interacted projection and cooperative hashing approach that considers multi-view interactions for enhanced clustering performance.

Findings

Outperforms state-of-the-art methods on five large-scale datasets

Achieves significant acceleration in CPU time

Produces more compact and distinguishable hash representations

Abstract

In recent years, anchor and hash-based multi-view clustering methods have gained attention for their efficiency and simplicity in handling large-scale data. However, existing methods often overlook the interactions among multi-view data and higher-order cooperative relationships during projection, negatively impacting the quality of hash representation in low-dimensional spaces, clustering performance, and sensitivity to noise. To address this issue, we propose a novel approach named Tensor-Interacted Projection and Cooperative Hashing for Multi-View Clustering(TPCH). TPCH stacks multiple projection matrices into a tensor, taking into account the synergies and communications during the projection process. By capturing higher-order multi-view information through dual projection and Hamming space, TPCH employs an enhanced tensor nuclear norm to learn more compact and distinguishable hash representations, promoting communication within and between views. Experimental results demonstrate that this refined method significantly outperforms state-of-the-art methods in clustering on five large-scale multi-view datasets. Moreover, in terms of CPU time, TPCH achieves substantial acceleration compared to the most advanced current methods. The code is available at \textcolor{red}{\url{https://github.com/jankin-wang/TPCH}}.