Please use this identifier to cite or link to this item:
Title: Data classification on multiple manifolds
Authors: Xiao, R
Zhao, Q
Zhang, D 
Shi, P
Issue Date: 2010
Source: Proceedings - International Conference on Pattern Recognition, 2010, p. 3898-3901 How to cite?
Abstract: Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach.
Description: 2010 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, 23-26 August 2010
ISBN: 9780769541099
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.949
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Dec 8, 2018

Page view(s)

Last Week
Last month
Citations as of Dec 17, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.