Unsupervised Learning of Image Manifolds by Semidefinite Programming Export

@article{citeulike:692342, abstract = {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.}, author = {Weinberger, Kilian and Saul, Lawrence}, citeulike-article-id = {692342}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1138215.1138217}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/s11263-005-4939-z}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/klu/visi/2006/00000070/00000001/00004939}, citeulike-linkout-3 = {http://www.springerlink.com/content/t21q747q278qx4x1}, day = {1}, doi = {10.1007/s11263-005-4939-z}, issn = {0920-5691}, journal = {International Journal of Computer Vision}, keywords = {dimension-reduction, image, manifold, spectral, unsupervised}, month = {October}, number = {1}, pages = {77--90}, posted-at = {2008-02-29 19:16:52}, priority = {2}, publisher = {Springer}, title = {Unsupervised Learning of Image Manifolds by Semidefinite Programming}, url = {http://dx.doi.org/10.1007/s11263-005-4939-z}, volume = {70}, year = {2006} }

TY - JOUR ID - citeulike:692342 L3 - citeulike-article-id:692342 N2 - 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. IS - 1 JF - International Journal of Computer Vision SN - 0920-5691 EP - 90 TI - Unsupervised Learning of Image Manifolds by Semidefinite Programming PB - Springer VL - 70 SP - 77 KW - dimension-reduction KW - image KW - manifold KW - spectral KW - unsupervised AU - Weinberger, Kilian AU - Saul, Lawrence PY - 2006/10/01/ UR - http://dx.doi.org/10.1007/s11263-005-4939-z ER -