Fused feature representation discovery for high-dimensional and sparse data

Jun Suzuki, Masaaki Nagata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The automatic discovery of a significant low-dimensional feature representation from a given data set is a fundamental problem in machine learning. This paper focuses specifically on the development of the feature representation discovery methods appropriate for high-dimensional and sparse data. We formulate our feature representation discovery problem as a variant of the semi-supervised learning problem, namely, as an optimization problem over unsupervised data whose ob-jective is evaluating the impact of each feature with respect to modeling a target task according to the initial model constructed by using supervised data. The most notable characteristic of our method is that it offers a feasible processing speed even if the numbers of data and features are both in the millions or even billions, and successfully provides a significantly small number of feature sets, i.e., fewer than 10, that can also offer improved performance compared with those obtained with the original feature sets. We demonstrate the effectiveness of our method in experiments consisting of two well-studied natural language processing tasks.

Original languageEnglish
Title of host publicationProceedings of the National Conference on Artificial Intelligence
PublisherAI Access Foundation
Pages1593-1599
Number of pages7
ISBN (Electronic)9781577356783
Publication statusPublished - 2014 Jan 1
Externally publishedYes
Event28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014 - Quebec City, Canada
Duration: 2014 Jul 272014 Jul 31

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume2

Other

Other28th AAAI Conference on Artificial Intelligence, AAAI 2014, 26th Innovative Applications of Artificial Intelligence Conference, IAAI 2014 and the 5th Symposium on Educational Advances in Artificial Intelligence, EAAI 2014
CountryCanada
CityQuebec City
Period14/7/2714/7/31

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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