Unsupervised feature selection via dual space-based low redundancy scores and extended OLSDA

Abstract

Spectral clustering is a widely used method for unsupervised feature selection (UFS) to generate pseudo labels. Nonetheless, it is acknowledged that graph algorithms suffer from issues such as redundancy and the dissatisfaction of connectivity, which greatly affect the quality of the learning local manifold. Moreover, existing UFS methods usually ignore the linear dependency among features and the role of non-negative semantics of the predicted labels. Hence, a novel algorithm using dual space-based low redundancy scores and extended orthogonal least square discriminant analysis (OLSDA), abbreviated as DLSEO, is proposed in this paper. Specifically, extended OLSDA is employed to derive non-negative clustering labels and a manifold structure, which avoids explicitly constructing a Laplacian graph. The dual space-based low redundancy scores related to mutual information eliminate redundant data and features, preventing their interference in the feature selection process. Moreover, ℓ2,1−2-norm is introduced to simultaneously guarantee the feature weight matrix's sparsity and ensure the selection of salient features. The results of experiments conducted on twelve benchmark datasets compared with several relevant methods show the superiority of DLSEO.