Unsupervised Feature Selection based on Adaptive Similarity Learning and Subspace Clustering

TL;DR

This paper introduces an unsupervised feature selection method that leverages adaptive similarity learning and subspace clustering to improve data representation and reduce complexity in learning tasks.

Contribution

It presents a novel approach combining subspace clustering with adaptive similarity learning for unsupervised feature selection, enhancing data representation.

Findings

Outperforms existing methods on benchmark datasets

Effectively preserves sample similarities and discriminative information

Demonstrates convergence and robustness of the proposed algorithm

Abstract

Feature selection methods have an important role on the readability of data and the reduction of complexity of learning algorithms. In recent years, a variety of efforts are investigated on feature selection problems based on unsupervised viewpoint due to the laborious labeling task on large datasets. In this paper, we propose a novel approach on unsupervised feature selection initiated from the subspace clustering to preserve the similarities by representation learning of low dimensional subspaces among the samples. A self-expressive model is employed to implicitly learn the cluster similarities in an adaptive manner. The proposed method not only maintains the sample similarities through subspace clustering, but it also captures the discriminative information based on a regularized regression model. In line with the convergence analysis of the proposed method, the experimental results on benchmark datasets demonstrate the effectiveness of our approach as compared with the state of the art methods.