Abstract
Principal Component Analysis (PCA) is a feature extraction approach directly based on a whole vector pattern and acquires a set of projections that can realize the best reconstruction for an original data in the mean squared error sense. In this paper, the progressive PCA (PrPCA) is proposed, which could progressively extract features from a set of given data with large dimensionality and the extracted features are subsequently applied to pattern recognition. Experiments on the FERET database show its face recognition performance is better than those based on both E(PC)2A and FLDA.
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References
Chen, S.C., Zhang, D.Q., Zhou, Z.-H.: Enhanced (PC)2A for face recognition with one training image per person. Pattern Recognition Letters (2004) (in press)
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Chen, S.C., Liu, J., Zhou, Z.-H.: Making FLDA applicable to face recognition with one sample per person. Pattern Recognition 37, 1553–1555 (2004)
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© 2004 Springer-Verlag Berlin Heidelberg
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Liu, J., Chen, S., Zhou, ZH. (2004). Progressive Principal Component Analysis. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_126
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DOI: https://doi.org/10.1007/978-3-540-28647-9_126
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22841-7
Online ISBN: 978-3-540-28647-9
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