Parameter-free entropy-regularized multi-view clustering with hierarchical feature selection

TL;DR

This paper introduces a parameter-free, entropy-regularized multi-view clustering framework with hierarchical feature selection, achieving high accuracy, efficiency, and automatic view and feature importance determination across diverse datasets.

Contribution

It proposes novel algorithms that replace manual parameters with entropy regularization, enabling adaptive cross-view consensus and hierarchical feature selection with convergence guarantees.

Findings

Outperforms 15 state-of-the-art methods on five benchmarks.

Achieves up to 97% computational efficiency gains.

Reduces feature dimensionality to 0.45% of original size.

Abstract

Multi-view clustering faces critical challenges in automatically discovering patterns across heterogeneous data while managing high-dimensional features and eliminating irrelevant information. Traditional approaches suffer from manual parameter tuning and lack principled cross-view integration mechanisms. This work introduces two complementary algorithms: AMVFCM-U and AAMVFCM-U, providing a unified parameter-free framework. Our approach replaces fuzzification parameters with entropy regularization terms that enforce adaptive cross-view consensus. The core innovation employs signal-to-noise ratio based regularization ($\delta_j^h = \frac{\bar{x}_j^h}{(\sigma_j^h)^2}$) for principled feature weighting with convergence guarantees, coupled with dual-level entropy terms that automatically balance view and feature contributions. AAMVFCM-U extends this with hierarchical dimensionality reduction operating at feature and view levels through adaptive thresholding ($\theta^{h^{(t)}} = \frac{d_h^{(t)}}{n}$). Evaluation across five diverse benchmarks demonstrates superiority over 15 state-of-the-art methods. AAMVFCM-U achieves up to 97% computational efficiency gains, reduces dimensionality to 0.45% of original size, and automatically identifies critical view combinations for optimal pattern discovery.