Unsupervised Learning of Image Manifolds by Semidefinite Programming

Authors:
Kilian Q. Weinberger;Lawrence K. Saul
Affiliations:
Department of Computer and Information Science, University of Pennsylvania, Philadelphia 19104-6389;Department of Computer and Information Science, University of Pennsylvania, Philadelphia 19104-6389
Venue:
International Journal of Computer Vision
Year:
2006

Citing 20
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Abstract

Can we detect low dimensional structure in high dimensional data sets of images? In this paper, we propose an algorithm for unsupervised learning of image manifolds by semidefinite programming. Given a data set of images, our algorithm computes a low dimensional representation of each image with the property that distances between nearby images are preserved. More generally, it can be used to analyze high dimensional data that lies on or near a low dimensional manifold. 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.