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

Author(s): Bhaskara, Aditya; Charikar, Moses; Moitra, Ankur; Vijayaraghavan, Aravindan

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dc.contributor.authorBhaskara, Aditya-
dc.contributor.authorCharikar, Moses-
dc.contributor.authorMoitra, Ankur-
dc.contributor.authorVijayaraghavan, Aravindan-
dc.date.accessioned2021-10-08T19:44:39Z-
dc.date.available2021-10-08T19:44:39Z-
dc.date.issued2014en_US
dc.identifier.citationBhaskara, 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.issn2640-3498-
dc.identifier.urihttp://proceedings.mlr.press/v35/bhaskara14b.html-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1w814-
dc.description.abstractFactor 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.extent1280 - 1282en_US
dc.language.isoen_USen_US
dc.relation.ispartofProceedings of The 27th Conference on Learning Theoryen_US
dc.relation.ispartofseriesProceedings of Machine Learning Research;-
dc.rightsFinal published version. Article is made available in OAR by the publisher's permission or policy.en_US
dc.titleOpen Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold?en_US
dc.typeConference Articleen_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/conference-proceedingen_US

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