A Self-Organizing Tensor Architecture for Multi-View Clustering

TL;DR

This paper introduces a tensor-based multi-view clustering method that captures higher-order feature interactions across views, outperforming existing approaches in real-world datasets.

Contribution

It proposes a novel multi-linear tensor approach for multi-view clustering that explicitly models inter-feature correlations across multiple views.

Findings

Outperforms state-of-the-art multi-view clustering methods

Effectively captures higher-order interactions among views

Demonstrates superior performance on real-world datasets

Abstract

In many real-world applications, data are often unlabeled and comprised of different representations/views which often provide information complementary to each other. Although several multi-view clustering methods have been proposed, most of them routinely assume one weight for one view of features, and thus inter-view correlations are only considered at the view-level. These approaches, however, fail to explore the explicit correlations between features across multiple views. In this paper, we introduce a tensor-based approach to incorporate the higher-order interactions among multiple views as a tensor structure. Specifically, we propose a multi-linear multi-view clustering (MMC) method that can efficiently explore the full-order structural information among all views and reveal the underlying subspace structure embedded within the tensor. Extensive experiments on real-world datasets demonstrate that our proposed MMC algorithm clearly outperforms other related state-of-the-art methods.