Unsupervised Feature Selection with Adaptive Structure Learning

TL;DR

This paper introduces a unified framework for unsupervised feature selection that iteratively refines data structures and feature subsets, leading to more accurate and informative feature selection.

Contribution

It proposes a novel adaptive structure learning approach that jointly optimizes data structures and feature selection, overcoming limitations of traditional methods.

Findings

Outperforms existing unsupervised feature selection methods on benchmark datasets.

Effectively captures true data structures by iterative refinement.

Enhances the selection of informative features through adaptive learning.

Abstract

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods.