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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
ASCII Text
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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
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@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
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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
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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