Semi-Supervised Top-k Feature Selection with a General Optimization Framework-Reference-Cited by-同舟云学术

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Semi-Supervised Top-k Feature Selection with a General Optimization Framework

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Multimedia and Expo (ICME)
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Author:

Xu Lei¹,Wang Rong²,Nie Feiping¹,Wu Jun³,Li Xuelong²

Affiliation:

1. Northwestern Polytechnical University,School of Computer Science,Xi'an,P.R. China

2. Northwestern Polytechnical University,School of Artificial Intelligence, OPtics and ElectroNics (iOPEN),Xi'an,P.R. China

3. Huawei Technologies, co. Ltd,Nanjing Research and Development Center,Nanjing,P.R. China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10219544/10219545/10219603.pdf?arnumber=10219603

Reference32 articles.

1. Coordinate descent algorithms

2. Semi-Supervised Feature Selection via Insensitive Sparse Regression with Application to Video Semantic Recognition

3. Semisupervised feature selection via spline regression for video semantic recognition;han;IEEE Trans Neural Networks Learn Syst,2015

4. A graph Laplacian based approach to semi-supervised feature selection for regression problems

5. ?2,1 -norm regularized discriminative feature selection for unsupervised learning;yang;Proc IJCAI,2011

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