Title :
Beyond the graphs: Semi-parametric semi-supervised discriminant analysis
Author :
Fei Wang ; Xin Wang ; Tao Li
Author_Institution :
Sch. of Comput. & Inf. Sci., Florida Int. Univ., Miami, FL, USA
Abstract :
Linear discriminant analysis (LDA) is a popular feature extraction method that has aroused considerable interests in computer vision and pattern recognition fields. The projection vectors of LDA is usually achieved by maximizing the between-class scatter and simultaneously minimizing the within-class scatter of the data set. However, in practice, there is usually a lack of sufficient labeled data, which makes the estimated projection direction inaccurate. To address the above limitations, in this paper, we propose a novel semi-supervised discriminant analysis approach. Unlike traditional graph based methods, our algorithm incorporates the geometric information revealed by both labeled and unlabeled data points in a semi-parametric way. Specifically, the final projections of the data points will contain two parts: a discriminant part learned by traditional LDA (or KDA) on the labeled points and a geometrical part learned by kernel PCA on the whole data set. Therefore we call our algorithm semi-parametric semi-supervised discriminant analysis (SSDA). Experimental results on face recognition and image retrieval tasks are presented to show the effectiveness of our method.
Keywords :
face recognition; feature extraction; image retrieval; principal component analysis; computer vision; face recognition; feature extraction; geometric information; image retrieval; kernel PCA; linear discriminant analysis; pattern recognition; projection vector; semiparametric semisupervised discriminant analysis; unlabeled data points; Algorithm design and analysis; Computer vision; Face recognition; Feature extraction; Kernel; Linear discriminant analysis; Pattern recognition; Principal component analysis; Scattering; Vectors;
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
Print_ISBN :
978-1-4244-3992-8
DOI :
10.1109/CVPR.2009.5206675