Fused feature representation discovery for high-dimensional and sparse data

Jun Suzuki, Masaaki Nagata

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルProceedings of the National Conference on Artificial Intelligence
出版社AI Access Foundation
ページ1593-1599
ページ数7
ISBN(電子版)9781577356783
出版ステータスPublished - 2014 1 1
外部発表はい
イベント28th 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
継続期間: 2014 7 272014 7 31

出版物シリーズ

名前Proceedings of the National Conference on Artificial Intelligence
2

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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