Joint feature weighting and adaptive graph-based matrix regression for image supervised feature Selection-Reference-Cited by-同舟云学术

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Joint feature weighting and adaptive graph-based matrix regression for image supervised feature Selection

Published:2021-01 Issue: Volume:90 Page:116044
ISSN:0923-5965
Container-title:Signal Processing: Image Communication
language:en
Short-container-title:Signal Processing: Image Communication

Author:

Lu Yun,Chen Xiuhong

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference42 articles.

1. A survey on semi–supervised feature selection methods;Sheikhpour;Pattern Recognit.,2017

2. Feature selection in mixed data;Zhang;Pattern Recognit.,2016

3. Marginal representation learning with graph structure self-adaptation;Zhang;IEEE Trans. Neural Netw. Learn. Syst.,2018

4. A unified feature selection framework for graph embedding on high dimensional data;Chen;IEEE Trans. Knowl. Data Eng.,2015

5. An introduction to variable and feature selection;Guton;J. Mach. Learn. Res.,2003

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1. Hyper-class representation of data;Neurocomputing;2022-09

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