Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICPR.2004.1334252
Title: Kernel autoassociator with applications to visual classification
Authors: Zhang, H.
Huang, W.
Huang, Z. 
Zhang, B.
Issue Date: 2004
Source: Zhang, H., Huang, W., Huang, Z., Zhang, B. (2004). Kernel autoassociator with applications to visual classification. Proceedings - International Conference on Pattern Recognition 2 : 443-446. ScholarBank@NUS Repository. https://doi.org/10.1109/ICPR.2004.1334252
Abstract: 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.
Source Title: Proceedings - International Conference on Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/41016
ISBN: 0769521282
ISSN: 10514651
DOI: 10.1109/ICPR.2004.1334252
Appears in Collections:Staff Publications

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