Unsupervised Learning of Manifolds via Linear Approximations

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	Similar Articles Articles by Hassan A. Kingravi Articles by M. Emre Celebi Articles by Pragya P. Rajauria

18th International Conference on Database and Expert Systems Applications (DEXA 2007)

Regensburg, Germany

September 03-September 07

ISBN: 0-7695-2932-1

	ASCII Text	x
	Hassan A. Kingravi, M. Emre Celebi, Pragya P. Rajauria, "Unsupervised Learning of Manifolds via Linear Approximations," Database and Expert Systems Applications, International Workshop on, pp. 54-58, 18th International Conference on Database and Expert Systems Applications (DEXA 2007), 2007.

	BibTex	x
	@article{ 10.1109/DEXA.2007.107, author = {Hassan A. Kingravi and M. Emre Celebi and Pragya P. Rajauria}, title = {Unsupervised Learning of Manifolds via Linear Approximations}, journal ={Database and Expert Systems Applications, International Workshop on}, volume = {0}, year = {2007}, issn = {1529-4188}, pages = {54-58}, doi = {http://doi.ieeecomputersociety.org/10.1109/DEXA.2007.107}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Database and Expert Systems Applications, International Workshop on TI - Unsupervised Learning of Manifolds via Linear Approximations SN - 1529-4188 SP54 EP58 A1 - Hassan A. Kingravi, A1 - M. Emre Celebi, A1 - Pragya P. Rajauria, PY - 2007 KW - clustering KW - ISOMAP KW - LPP KW - LLE KW - manifold learning KW - Laplacian KW - Eigenmap KW - k-means VL - 0 JA - Database and Expert Systems Applications, International Workshop on ER -

Hassan A. Kingravi, Texas A&M University, USA

M. Emre Celebi, Louisiana State University in Shreveport, USA

Pragya P. Rajauria, University of Bridgeport, USA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/DEXA.2007.107

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

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