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Kernel Autoassociator with Applications to Visual Classification

9 years 6 months ago
Kernel Autoassociator with Applications to Visual Classification
Autoassociator is an important issue in concept learning, and the learned concept of a particular class can be used to distinguish the class from the others. For nonlinear autoassociation, this paper presents a new model referred to as kernel autoassociator. Using kernel feature space as a potential nonlinear manifold, the model formulates the autoassociation as a special reconstruction problem from kernel feature space to input space. Two methods are developed to solve the problem. We evaluate the autoassociator with artificial data, and apply it to handwritten digit recognition and multiview face recognition, yielding positive experimental results.
Bailing Zhang, Haihong Zhang, Weimin Huang, Zhiyon
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2004
Where ICPR
Authors Bailing Zhang, Haihong Zhang, Weimin Huang, Zhiyong Huang
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