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

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 tha t enforce adaptive cross - view consensus. The core innovation employs signal - to - noise ratio based regularization 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 . 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. Keywords: Multi - view clustering, Dimensionality reduction, Feature selection, Parameter - free, Signal - to - noise ratio, Fuzzy c - means 1. Introduction Understanding complex data is crucial in today's data - driven world, and recent advancements in machine learning are significantly enhancing our ability to analyze and interpret this information.