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Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
2011
IEEE Transactions on Knowledge and Data Engineering
Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feature extraction. Recently, unlabeled data have been utilized to improve LDA. However, the intrinsic problems of LDA still exist and only the similarity among the unlabeled data is utilized. In this paper, we propose a novel algorithm, called Semisupervised Semi-Riemannian Metric Map (S 3 RMM), following the geometric
doi:10.1109/tkde.2010.143
fatcat:oycbi3jb7rahtnxuoapulvv3v4