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ICPR
2008
IEEE

Semi-supervised marginal discriminant analysis based on QR decomposition

14 years 5 months ago
Semi-supervised marginal discriminant analysis based on QR decomposition
In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neighborhood relations between the data points from the same class, while maximizing the margin between the neighboring data points with different class labels. Different from traditional dimensionality reduction algorithms like linear discriminant analysis (LDA) and maximum margin criterion (MMC) which seeks only the global Euclidean structure, SMDA takes local structure of the data into account. Moreover, it is designed for semisupervised learning which incorporates both labeled and unlabeled data points and avoids suffering the small sample size (SSS) problem. QR decomposition is then employed to find the optimal transformation which makes the algorithm scalable and more efficient. Experiments on face recognition are presented to show the effectiveness of the method.
Rui Xiao, Pengfei Shi
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICPR
Authors Rui Xiao, Pengfei Shi
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