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01 May 2006
Unsupervised Learning of Image Manifolds by Semidefinite Programming
 Kilian Q. Weinberger,
 Lawrence K. Saul
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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.
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 Title
 Unsupervised Learning of Image Manifolds by Semidefinite Programming
 Journal

International Journal of Computer Vision
Volume 70, Issue 1 , pp 7790
 Cover Date
 20061001
 DOI
 10.1007/s112630054939z
 Print ISSN
 09205691
 Online ISSN
 15731405
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 manifold learning
 dimensionality reduction
 semidefinite programming
 kernel methods
 data analysis
 image manifolds
 semidefinite embedding
 Industry Sectors
 Authors

 Kilian Q. Weinberger ^{(1)}
 Lawrence K. Saul ^{(1)}
 Author Affiliations

 1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, 191046389