Unsupervised learning of image manifolds by semidefinite programming
Weinberger, K.Q.; Saul, L.K.
Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on
Volume 2, Issue , 27 June-2 July 2004 Page(s): II-988 - II-995 Vol.2
Digital Object Identifier   10.1109/CVPR.2004.1315272
Summary: 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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