Fuzzy-weighted Group-aware Unsupervised Multi-view Feature Selection for Multi-omics Data

The high dimensionality and inherent noise in multi-omics data complicate analysis, making multi-view feature selection a vital solution for integrating heterogeneous data. However, existing methods still suffer from the following limitations: (i) They neglect structural information among features within individual views; (ii) They apply uniform penalties to all features, failing to distinguish between the importance of feature groups and independent features. This one-size-fits-all strategy may over-penalize cohesive feature groups containing useful information or retain less informative features. Furthermore, direct computation of pairwise fuzzy mutual information is expensive in high dimensions. To this end, this paper proposes a Fuzzy-weighted Group-aware Unsupervised Multi-view Feature Selection (FWGAFS) method. This method adaptively penalizes features and feature groups according to their importance, with penalty strength inversely proportional to their evaluated significance. The resulting regularization is intended to favor informative features and reduce the influence of less informative ones. Specifically, FWGAFS first employs density-based clustering to partition features into groups, capturing view-specific omics structures. It then uses an anchor-based strategy to reduce the computational overhead of fuzzy mutual information. Subsequently, a fuzzy-weighted group-aware regularization term is formulated to implement differentiated penalties. Concurrently, FWGAFS integrates unsupervised clustering with graph learning to capture underlying data structures, thereby guiding the selection of structurally relevant features. An alternating optimization algorithm solves the proposed model. Experiments on real-world multi-omics datasets show that FWGAFS achieves competitive overall performance compared with the state-of-the-art methods.