Presentation 2014-11-17
Multitask Learning Meets Tensor Factorization : Task Imputation via Convex Optimization
Kishan WIMALAWARNE, Masashi SUGIYAMA, Ryota TOMIOKA,
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Abstract(in English) We study a multitask learning problem in which each task is parametrized by a weight vector and indexed by a pair of indices, which can be e.g, (consumer, time). The weight vectors can be collected into a tensor and the (multilinear-)rank of the tensor controls the amount of sharing of information among tasks. Two types of convex relaxations have recently been proposed for the tensor multilinear rank. However, we argue that both of them are not optimal in the context of multitask learning in which the dimensions or multilinear rank are typically inhomogeneous. We propose a new norm, which we call the scaled latent trace norm and analyze the excess risk of all the three norms. Both the theory and experiments support the advantage of the new norm when the tensor is not equal-sized and we do not a priori know which mode is low rank.
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Keyword(in English) Multitask learning / scaled latent norm / tensor
Paper # IBISML2014-49
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Committee IBISML
Conference Date 2014/11/10(1days)
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Registration To Information-Based Induction Sciences and Machine Learning (IBISML)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Multitask Learning Meets Tensor Factorization : Task Imputation via Convex Optimization
Sub Title (in English)
Keyword(1) Multitask learning
Keyword(2) scaled latent norm
Keyword(3) tensor
1st Author's Name Kishan WIMALAWARNE
1st Author's Affiliation Department of Computer Science, Tokyo Institute of Technology()
2nd Author's Name Masashi SUGIYAMA
2nd Author's Affiliation Department of Complexity Science and Engineering, University of Tokyo
3rd Author's Name Ryota TOMIOKA
3rd Author's Affiliation The Toyota Technological Institute at Chicago
Date 2014-11-17
Paper # IBISML2014-49
Volume (vol) vol.114
Number (no) 306
Page pp.pp.-
#Pages 8
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