Abstract
In subspace pattern recognition, the basis vectors represent the features of the data and define the class. In the previous works, standard principal component analysis is used to derive the basis vectors. Compared with standard PCA, Nonlinear PCA can provide the high-order statistics and result in non-orthogonal basis vectors. We combine Nonlinear PCA and a subspace classifier to extract the edge and line features in an image. The simulation results indicate that the basis vectors from Nonlinear PCA can classify the edge patterns better than those from linear PCA.
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© 2004 Springer-Verlag Berlin Heidelberg
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Chen, YW., Zeng, XY. (2004). Image Feature Representation by the Subspace of Nonlinear PCA. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3214. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30133-2_43
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DOI: https://doi.org/10.1007/978-3-540-30133-2_43
Publisher Name: Springer, Berlin, Heidelberg
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