Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking

TL;DR

This paper introduces an unsupervised feature selection algorithm leveraging dual manifold re-ranking to better capture data structure and improve feature selection accuracy in high-dimensional data.

Contribution

It proposes a novel dual manifold re-ranking method that models the relationships between samples and features for enhanced unsupervised feature selection.

Findings

Outperforms three original unsupervised feature selection algorithms.

Effectively captures the dual relationship between samples and features.

Improves feature selection by considering sample importance and manifold structures.

Abstract

High-dimensional data is commonly encountered in numerous data analysis tasks. Feature selection techniques aim to identify the most representative features from the original high-dimensional data. Due to the absence of class label information, it is significantly more challenging to select appropriate features in unsupervised learning scenarios compared to supervised ones. Traditional unsupervised feature selection methods typically score the features of samples based on certain criteria, treating samples indiscriminately. However, these approaches fail to fully capture the internal structure of the data. The importance of different samples should vary, and there is a dual relationship between the weight of samples and features that will influence each other. Therefore, an unsupervised feature selection algorithm based on dual manifold re-ranking (DMRR) is proposed in this paper. Different similarity matrices are constructed to depict the manifold structures among samples, between samples and features, and among features themselves. Then, manifold re-ranking is performed by combining the initial scores of samples and features. By comparing DMRR with three original unsupervised feature selection algorithms and two unsupervised feature selection post-processing algorithms, experimental results confirm that the importance information of different samples and the dual relationship between sample and feature are beneficial for achieving better feature selection.