Paper Info
Title
Orthogonally constrained matrix factorization for robust unsupervised feature selection with local preserving
Abstract
Feature selection is a significant preprocessing technique that involves discarding redundant and irrelevant features, so as to reduce the data dimensionality and build a better understanding of data. Pruning superfluous features tend to build better generalization models while improving the computation efficiency extremely. In practices, obtaining the labels of data is time-consuming and labor-intensive, which brings great challenges for feature selection. In this paper, we present a novel robust unsupervised feature selection method for unlabeled dimensionality reduction, which obtains feature importance information by predicting cluster labels of data with matrix factorization. The orthogonal constraints on two decomposing matrices facilitate achieving more accurate class labels of clusters so as to select features with highly discrimination power. Then, the local preserving term is integrated into the projected data for selecting features with local retention ability. Independent of that, an alternative iterative algorithm is incorporated into the optimization of ℓ2,1-norm objective function for efficient and robust feature selection. Extensive comparative experiments are carried out on six benchmark datasets to evaluate the performance of the proposed method. The results show that our method outperforms several well-known unsupervised feature selection methods in terms of both clustering accuracy and normalized mutual information.
Year
DOI
Venue
2022
10.1016/j.ins.2021.11.068
Information Sciences
Keywords
DocType
Volume
Unsupervised feature selection,Matrix factorization,Local preserving,ℓ21-norm,Orthogonal constraints
Journal
586
ISSN
Citations 
PageRank 
0020-0255
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Chuan Luo1694.74
Jian Zheng200.68
Tianrui Li322.04
Hongmei Chen402.03
Yanyong Huang5193.57
xi peng6966.39