.png){kind=icon}
.png){kind=icon}
Return
Multi-class feature selection by exploring reliable class correlation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.107377.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
High-dimensional multi-class data are ubiquitous in many domains and often have complicated but semantic relevance among classes. Preserving these semantic relationships in the process of feature selection is crucial to learn the nature of the objects under study. Many existing similarity preserving based feature selection methods employ fixed similarities learned from the original feature space. However, the existence of redundant and irrelevant features in the original feature space may make the similarities unreliable to guide feature selection. In this paper, we propose to learn a dynamic similarity matrix P and a feature weighting matrix W with a two-stage method. P is calculated in the dimension reduction feature space according to W, then fixed and used to guide the next round of feature selection. Similarity matrix P and feature weighting matrix W will gradually become reliable after some repeat of the two stages. In this way, the proposed approach tries to capture reliable class correlation and select target features. Experiments on the data with noisy features and various types of public data show the attractiveness of the proposed method. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Multi-class learning
Feature selection
Reliable class correlation
Dimension reduction feature space
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.