| Paper: | MP-P1.5 |
| Session: | Image and Video Storage and Retrieval II |
| Time: | Monday, September 17, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
CLASSIFICATION BY CHEEGER CONSTANT REGULARIZATION |
| Authors: |
Hsun-Hsien Chang; Carnegie Mellon University | | |
| | José M. F. Moura; Carnegie Mellon University | | |
| Abstract: |
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. |
