Semi-supervised Discriminant Analysis Based on Sparse-coding Theory

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	Semi-supervised Discriminant Analysis Based on Sparse-coding Theory; Semi-supervised Discriminant Analysis Based on Sparse-coding Theory
	Qi Zhang ; Tianguang Chu
	2016
关键词	Semi-supervised leaning feature extraction sparse coding face recognition Semi-supervised leaning feature extraction sparse coding face recognition
英文摘要	We consider the problem of semi-supervised graphbased learning.Since in semi-supervised settings,the labeled information is limited,we first propose l_α-norm-based label propagation(a-SLP) model to estimate the soft labels by using small set of labeled and large amount of unlabeled training data,and thereby enrich the supervised information.Based on the α-SLP results,we conduct semi-supervised discriminant analysis and present graph-based embedding(SGE) approach by incorporating the estimated soft labels with the local geometric information of both the within-class and between-class training data.Within-class affinity matrices and between-class weight matrix are introduced to preserve the propagated label information and local geometric information of data.This gets rid of the problem that merely concerning about the soft labels may lead to errors.By minimizing the locality-preserved within-class distances and maximizing the weighted betweenclass separability,subspaces that characterize the intrinsic data str...; We consider the problem of semi-supervised graphbased learning.Since in semi-supervised settings,the labeled information is limited,we first propose l_α-norm-based label propagation(a-SLP) model to estimate the soft labels by using small set of labeled and large amount of unlabeled training data,and thereby enrich the supervised information.Based on the α-SLP results,we conduct semi-supervised discriminant analysis and present graph-based embedding(SGE) approach by incorporating the estimated soft labels w; 中国自动化学会控制理论专业委员会、中国系统工程学会; 6
会议录	第35届中国控制会议
语种	英语
内容类型	会议论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/479916]
专题	工学院
推荐引用方式 GB/T 7714	Qi Zhang,Tianguang Chu. Semi-supervised Discriminant Analysis Based on Sparse-coding Theory, Semi-supervised Discriminant Analysis Based on Sparse-coding Theory[C]. 见:.