Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?
Author(s): Bhaskara, Aditya; Charikar, Moses; Moitra, Ankur; Vijayaraghavan, Aravindan
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Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bhaskara, Aditya | - |
dc.contributor.author | Charikar, Moses | - |
dc.contributor.author | Moitra, Ankur | - |
dc.contributor.author | Vijayaraghavan, Aravindan | - |
dc.date.accessioned | 2021-10-08T19:44:39Z | - |
dc.date.available | 2021-10-08T19:44:39Z | - |
dc.date.issued | 2014 | en_US |
dc.identifier.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. | en_US |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | http://proceedings.mlr.press/v35/bhaskara14b.html | - |
dc.identifier.uri | http://arks.princeton.edu/ark:/88435/pr1w814 | - |
dc.description.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. | en_US |
dc.format.extent | 1280 - 1282 | en_US |
dc.language.iso | en_US | en_US |
dc.relation.ispartof | Proceedings of The 27th Conference on Learning Theory | en_US |
dc.relation.ispartofseries | Proceedings of Machine Learning Research; | - |
dc.rights | Final published version. Article is made available in OAR by the publisher's permission or policy. | en_US |
dc.title | Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold? | en_US |
dc.type | Conference Article | en_US |
pu.type.symplectic | http://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceeding | en_US |
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TensorDecompositionsUniquenessThreshold.pdf | 125.67 kB | Adobe PDF | View/Download |
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