Please use this identifier to cite or link to this item:
|Title:||Kernel autoassociator with applications to visual classification|
|Citation:||Zhang, H., Huang, W., HUANG ZHIYONG, 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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 15, 2019
WEB OF SCIENCETM
checked on Jan 7, 2019
checked on Jan 13, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.