ML Anthology

Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?

Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan
COLT 2014 pp. 1280-1282
/colt/2014/bhaskara2014colt-open/

Abstract

Factor analysis is a basic tool in statistics and machine learning, where the goal is to take many variables and explain them away using fewer unobserved variables, called factors. It was introduced in a pioneering study by psychologist Charles Spearman, who used it to test his theory that there are fundamentally two types of intelligence – verbal and mathematical. This study has had a deep

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Text

Bhaskara et al. "Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?." Annual Conference on Computational Learning Theory, 2014.

Markdown

[Bhaskara et al. "Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?." Annual Conference on Computational Learning Theory, 2014.](https://mlanthology.org/colt/2014/bhaskara2014colt-open/)

BibTeX

@inproceedings{bhaskara2014colt-open,
  title     = {{Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?}},
  author    = {Bhaskara, Aditya and Charikar, Moses and Moitra, Ankur and Vijayaraghavan, Aravindan},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {2014},
  pages     = {1280-1282},
  url       = {https://mlanthology.org/colt/2014/bhaskara2014colt-open/}
}