Departmental Papers (CIS)

Date of this Version

6-27-2004

Document Type

Conference Paper

Comments

Copyright © 2004 IEEE. Reprinted from Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, held 27 June - 2 July 2004. Volume 2, pages 988-995. Publisher URL: http://dx.doi.org/10.1109/CVPR.2004.1315272

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Abstract

Can we detect low dimensional structure in high dimensional data sets of images and video? The problem of dimensionality reduction arises often in computer vision and pattern recognition. In this paper, we propose a new solution to this problem based on semidefinite programming. Our algorithm can be used to analyze high dimensional data that lies on or near a low dimensional manifold. It overcomes certain limitations of previous work in manifold learning, such as Isomap and locally linear embedding. We illustrate the algorithm on easily visualized examples of curves and surfaces, as well as on actual images of faces, handwritten digits, and solid objects.

Keywords

computer vision, pattern recognition

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Date Posted: 26 July 2004

This document has been peer reviewed.