ML Anthology

Random Matrices in Data Analysis

Dimitris Achlioptas
ECML-PKDD 2004 pp. 1-7
doi:10.1007/978-3-540-30115-8_1 /ecmlpkdd/2004/achlioptas2004ecml-random/

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.

PDF ECML-PKDD Semantic Scholar

Cite

Text

Achlioptas. "Random Matrices in Data Analysis." European Conference on Machine Learning, 2004. doi:10.1007/978-3-540-30115-8_1

Markdown

[Achlioptas. "Random Matrices in Data Analysis." European Conference on Machine Learning, 2004.](https://mlanthology.org/ecmlpkdd/2004/achlioptas2004ecml-random/) doi:10.1007/978-3-540-30115-8_1

BibTeX

@inproceedings{achlioptas2004ecml-random,
  title     = {{Random Matrices in Data Analysis}},
  author    = {Achlioptas, Dimitris},
  booktitle = {European Conference on Machine Learning},
  year      = {2004},
  pages     = {1-7},
  doi       = {10.1007/978-3-540-30115-8_1},
  url       = {https://mlanthology.org/ecmlpkdd/2004/achlioptas2004ecml-random/}
}