Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-supervised metric learning by maximizing constraint margin
Proceedings of the 17th ACM conference on Information and knowledge management
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We propose a new method, called Subclass-oriented Dimension Reduction with Pairwise Constraints (SODRPaC), for dimension reduction on high dimensional data Current linear semi-supervised dimension reduction methods using pairwise constraints, e.g., must-link constraints and cannot-link constraints, can not handle appropriately the data of multiple subclasses where the points of a class are separately distributed in different groups To illustrate this problem, we particularly classify the must-link constraint into two categories, which are the inter-subclass must-link constraint and the intra-subclass must-link constraint, respectively We argue that handling the inter-subclass must-link constraint is challenging for current discriminant criteria Inspired by the above observation and the cluster assumption that nearby points are possible in the same class, we carefully transform must-link constraints into cannot-link constraints, and then propose a new discriminant criterion by employing the cannot-link constraints and the compactness of shared nearest neighbors For the reason that the local data structure is one of the most significant features for the data of multiple subclasses, manifold regularization is also incorporated in our dimension reduction framework Extensive experiments on both synthetic and practical data sets illustrate the effectiveness of our method.