Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction

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Semi-supervised double sparse graphs based discriminant analysis for dimensionality reduction

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作者：Puhua Chen^a ; ^{huaer1986710@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Licheng Jiao^a ; ^{lchjiao@mail.xidian.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Fang Liu^b ; ^{f63liu@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jiaqi Zhao^a ; ^{jiaqizhao88@126.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Zhiqiang Zhao^a ; ^{zhiq_zhao@yeah.net" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Shuai Liu^a ; ^{shliu_122908@yahoo.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
关键词：Semi-supervised learning ; Discriminant analysis ; Dimensionality reduction ; Sparse graph ; Graph embedding
刊名：Pattern Recognition
出版年：2017
出版时间：January 2017
年：2017
卷：61
期：Complete
页码：361-378
全文大小：2681 K

文摘

•: Semi-supervised double sparse graphs are explored.
•: Joint k-nearest-neighbor selection strategy is proposed.
•: Two different semi-supervised dimensionality reduction methods are proposed.

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