Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Linear Discriminant Analysis of MPF for Face Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Journal of Cognitive Neuroscience
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Face Recognition Based on Two Dimension Double PCA and Affinity Propagation
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 05
Manifold learning for video-to-video face recognition
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
LPP solution schemes for use with face recognition
Pattern Recognition
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In this paper, we proposed a weighted PCA (WPCA) method. This method first uses the distances between the test sample and each training sample to calculate the 'weighted' covariance matrix. It then exploits the obtained covariance matrix to perform feature extraction. The experimental results show that the proposed method can obtain a high accuracy than conventional PCA. WPCA has the underlying theoretical foundation: through the 'weighted' covariance matrix, WPCA takes emphasis on the training samples that are very close to the test sample and reduce the influence of the other training samples. As a result, it is likely that the test sample is easier to be classified into the same class as the training samples that are very close to it. The experimental results show the feasibility and effectiveness of WPCA.