%0 Conference Paper %1 DBLP:conf/ecml/Achlioptas04 %A Achlioptas, Dimitris %B ECML %D 2004 %E Boulicaut, Jean-Francois %E Esposito, Floriana %E Giannotti, Fosca %E Pedreschi, Dino %I Springer %K matrix random_matrix singular_value_decomposition %P 1-7 %R 10.1007/978-3-540-30115-8_1 %T Random Matrices in Data Analysis %V 3201 %X We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certain kernel methods. %@ 3-540-23105-6

@inproceedings{DBLP:conf/ecml/Achlioptas04, abstract = {We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certain kernel methods.}, added-at = {2010-04-29T15:41:29.000+0200}, author = {Achlioptas, Dimitris}, bibsource = {DBLP, http://dblp.uni-trier.de}, biburl = {https://www.bibsonomy.org/bibtex/2c9abf826801cf9b1355b6c35a57879f5/ytyoun}, booktitle = {ECML}, crossref = {DBLP:conf/ecml/2004}, doi = {10.1007/978-3-540-30115-8_1}, editor = {Boulicaut, Jean-Fran\c{c}ois and Esposito, Floriana and Giannotti, Fosca and Pedreschi, Dino}, interhash = {48d6f3774bd191a13bb52eefe2c162db}, intrahash = {c9abf826801cf9b1355b6c35a57879f5}, isbn = {3-540-23105-6}, keywords = {matrix random_matrix singular_value_decomposition}, pages = {1-7}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2016-06-14T14:05:16.000+0200}, title = {Random Matrices in Data Analysis}, volume = 3201, year = 2004 }