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Unified Subspace Analysis for Face Recognition
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Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
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Geometric Mean for Subspace Selection
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Transductive Component Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Iterative subspace analysis based on feature line distance
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Deterministic Column-Based Matrix Decomposition
IEEE Transactions on Knowledge and Data Engineering
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Novel maximum-margin training algorithms for supervised neural networks
IEEE Transactions on Neural Networks
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
Discriminant Locally Linear Embedding With High-Order Tensor Data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Common Spatial Pattern (CSP) is one of the most widespread methods for Brain-Computer Interfaces (BCI), which is capable of enhancing the separability of the brain signals such as multi-channel electroencephalogram (EEG). CSP attempts to strengthen the separability by maximizing the variance of the spatially filtered signal of one class while minimizing it for another class. A straightforward way to improve the CSP is to employ the Fisher-Rao linear discriminant analysis (FLDA). But for the two-class scenario in BCI, FLDA merely result in as small as one filter. Experimental results have shown that the number of spatial filter is too small to achieve satisfying classification accuracy. Therefore, more than one filter is expected to get better performance. To deal with this difficulty, in this paper we propose to divide each class into many sub-classes (clusters) and formulate the problem in a re-designed graph embedding framework where the vertexes are cluster centers. We also reformulate the traditional FLDA in our graph embedding framework, which helps developing and understanding the proposed method. Experimental results demonstrate the advantages of the proposed method.