A new graph-preserving unsupervised feature selection embedding LLE with low-rank constraint and feature-level representation

Unsupervised feature selection is a powerful tool to process high-dimensional data, in which a subset of features is selected out for effective data representation. In this paper, we proposes a novel robust unsupervised features selection method based on graph-preserving feature selection embedding LLE. Specifically, we integrate the graph matrix learning and the low-dimensional space learning together to identify the correlation among both features and samples from the intrinsic low-dimensional space of original data. Also, the global and local correlation of features have been taken into consideration through the low-rank constraint and the feature-level representation property to find lower-dimensional representation which preserves not only the global and local correlation of features but also the global and local structure of training samples. Furthermore, we propose a new optimization algorithm to the resulting objective function, which iteratively updates the graph matrix and the intrinsic space in order to collaboratively improve each of them. Experimental analysis on 18 benchmark datasets verified that our proposed method outperformed the state-of-the-art feature selection methods in terms of classification and clustering performance.