%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 }