By Topic

Classification by Cheeger Constant Regularization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
Hsun-Hsien Chang ; Carnegie Mellon Univ., Pittsburgh ; Moura, J.M.F.

This paper develops a classification algorithm in the framework of spectral graph theory where the underlying manifold of a high dimensional data set is described by a graph. The classification on the data is performed on the graph. The classifier optimizes an objective functional that combines prior information with the Cheeger constant. We interpret this approach as a regularized version of the Cheeger constant based classifier that we introduced recently. Our derivation shows that Cheeger regularization removes noise like a Laplacian based classifier but preserves better sharp boundaries needed for class separation. Experimental results show good performance of our proposed approach for classification applications.

Published in:

Image Processing, 2007. ICIP 2007. IEEE International Conference on  (Volume:2 )

Date of Conference:

Sept. 16 2007-Oct. 19 2007