Abstract
Optimal Component Analysis (OCA) is a linear subspace technique for dimensionality reduction designed to optimize object classification and recognition performance. The linear nature of OCA often limits recognition performance, if the underlying data structure is nonlinear or cluster structures are complex. To address these problems, we investigate a kernel analogue of OCA, which consists of applying OCA techniques to the data after it has been mapped nonlinearly into a new feature space, typically a high (possibly infinite) dimensional Hilbert space. In this paper, we study both the theoretical and algorithmic aspects of the problem and report results obtained in several object recognition experiments.
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Liu, X., Mio, W. (2005). Kernel Methods for Nonlinear Discriminative Data Analysis. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_38
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DOI: https://doi.org/10.1007/11585978_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30287-2
Online ISBN: 978-3-540-32098-2
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