One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space.(mehr)
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%0 Journal Article
%1 Belkin02laplacianeigenmaps
%A Belkin, Mikhail
%A Niyogi, Partha
%D 2002
%J Neural Computation
%K Embeddings spectral-methods
%P 1373--1396
%T Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
%U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.5888
%V 15
%X One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space.
@article{Belkin02laplacianeigenmaps,
abstract = {One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low dimensional manifold embedded in a high dimensional space.},
added-at = {2009-08-14T16:54:31.000+0200},
author = {Belkin, Mikhail and Niyogi, Partha},
biburl = {https://www.bibsonomy.org/bibtex/26cc3083b47772f6e33dec1a1c6e4f866/ahmedjawwad4u},
description = {CiteSeerX — Laplacian Eigenmaps for Dimensionality Reduction and Data Representation},
interhash = {210c3bcbbb2f38d6eecc6f95d6e14a87},
intrahash = {6cc3083b47772f6e33dec1a1c6e4f866},
journal = {Neural Computation},
keywords = {Embeddings spectral-methods},
pages = {1373--1396},
timestamp = {2009-08-14T16:55:11.000+0200},
title = {Laplacian Eigenmaps for Dimensionality Reduction and Data Representation},
url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.9.5888},
volume = 15,
year = 2002
}