ML Anthology

Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision

Amnon Shashua, Tamir Hazan
ICML 2005 pp. 792-799
doi:10.1145/1102351.1102451 /icml/2005/shashua2005icml-non/

Abstract

We derive algorithms for finding a non-negative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii) sparse image coding in computer vision, and (iii) model selection problems. We derive a "direct" positive-preserving gradient descent algorithm and an alternating scheme based on repeated multiple rank-1 problems.

PDF ICML Semantic Scholar

Cite

Text

Shashua and Hazan. "Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102451

Markdown

[Shashua and Hazan. "Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/shashua2005icml-non/) doi:10.1145/1102351.1102451

BibTeX

@inproceedings{shashua2005icml-non,
  title     = {{Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision}},
  author    = {Shashua, Amnon and Hazan, Tamir},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {792-799},
  doi       = {10.1145/1102351.1102451},
  url       = {https://mlanthology.org/icml/2005/shashua2005icml-non/}
}