Unsupervised learning of image manifolds by semidefinite programming
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pattern recognition
Artificial Intelligence and Robotics
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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.
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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 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=29134&page=9 This material is posted here with permission of the IEEE. Such permissionof the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personaluse of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.