ML Anthology

Matrix Factorization as Search

Kristian Kersting, Christian Bauckhage, Christian Thurau, Mirwaes Wahabzada
ECML-PKDD 2012 pp. 850-853
doi:10.1007/978-3-642-33486-3_62 /ecmlpkdd/2012/kersting2012ecmlpkdd-matrix/

Abstract

Simplex Volume Maximization (SiVM) exploits distance geometry for efficiently factorizing gigantic matrices. It was proven successful in game, social media, and plant mining. Here, we review the distance geometry approach and argue that it generally suggests to factorize gigantic matrices using search-based instead of optimization techniques.

PDF ECML-PKDD Semantic Scholar

Cite

Text

Kersting et al. "Matrix Factorization as Search." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33486-3_62

Markdown

[Kersting et al. "Matrix Factorization as Search." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/kersting2012ecmlpkdd-matrix/) doi:10.1007/978-3-642-33486-3_62

BibTeX

@inproceedings{kersting2012ecmlpkdd-matrix,
  title     = {{Matrix Factorization as Search}},
  author    = {Kersting, Kristian and Bauckhage, Christian and Thurau, Christian and Wahabzada, Mirwaes},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2012},
  pages     = {850-853},
  doi       = {10.1007/978-3-642-33486-3_62},
  url       = {https://mlanthology.org/ecmlpkdd/2012/kersting2012ecmlpkdd-matrix/}
}