18th International Conference on Database and Expert Systems Applications (DEXA 2007)
Unsupervised Learning of Manifolds via Linear Approximations
Regensburg, Germany
September 03-September 07
ISBN: 0-7695-2932-1
In this paper, we examine the application of manifold learning to the clustering problem. The method used is Locality Preserving Projections (LPP), which is chosen because of its computational efficiency. A detailed derivation of the method is presented, as well as the theoretical justi- fication behind it. Experiments performed on CMU's PIE database show that the projections created by LPP yield better clustering results than those obtained by k-means alone.
Index Terms:
clustering, ISOMAP, LPP, LLE, manifold learning, Laplacian, Eigenmap, k-means
Citation:
Hassan A. Kingravi, M. Emre Celebi, Pragya P. Rajauria, "Unsupervised Learning of Manifolds via Linear Approximations," dexa, pp.54-58, 18th International Conference on Database and Expert Systems Applications (DEXA 2007), 2007