Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?
Author(s): Bhaskara, Aditya; Charikar, Moses; Moitra, Ankur; Vijayaraghavan, Aravindan
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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 influence on modern psychology, to this day. However there is a serious mathematical limitation to this approach, which we describe next. |
Publication Date: | 2014 |
Citation: | Bhaskara, Aditya, Moses Charikar, Ankur Moitra, and Aravindan Vijayaraghavan. "Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?." Proceedings of The 27th Conference on Learning Theory 35 (2014): 1280-1282. |
ISSN: | 2640-3498 |
Pages: | 1280 - 1282 |
Type of Material: | Conference Article |
Series/Report no.: | Proceedings of Machine Learning Research; |
Journal/Proceeding Title: | Proceedings of The 27th Conference on Learning Theory |
Version: | Final published version. Article is made available in OAR by the publisher's permission or policy. |
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