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	Semi-supervised discriminant analysis via spectral transduction
	Zhai, Deming ; Chang, Hong ; Li, Bo ; Shan, Shiguang ; Chen, Xilin ; Gao, Wen
	2009
英文摘要	Linear Discriminant Analysis (LDA) is a popular method for dimensionality reduction and classification. In real-world applications when there is no sufficient labeled data, LDA suffers from serious performance drop or even fails to work. In this paper, we propose a novel method called Spectral Transduction Semi-Supervised Discriminant Analysis (STSDA), which can alleviate such problem by utilizing both labeled and unla-beled data. Our method takes into consideration both label augmenting and local structure preserving. First, we formulate label transduction with labeled and unlabeled data as a constrained convex optimization problem and solve it efficiently with a closed-form solution by using orthogonal projector matrices. Then, unlabeled data with reliable class estimations are selected with a balanced strategy to augment the original labeled data set. At last, LDA with manifold regularization is performed. Experimental results on face recognition demonstrate the effectiveness of our proposed method. ? 2009. The copyright of this document resides with its authors.; EI; 0
语种	英语
DOI标识	10.5244/C.23.32
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/411267]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Zhai, Deming,Chang, Hong,Li, Bo,et al. Semi-supervised discriminant analysis via spectral transduction. 2009-01-01.

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