Basis vector orthogonalization for an improved kernel gradient matching pursuit method

Yotaro Kubo*, Shinji Watanabe, Atsushi Nakamura, Simon Wiesler, Ralf Schlueter, Hermann Ney

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

With the aim of achieving a computationally efficient optimization of kernel-based probabilistic models for various problems, such as sequential pattern recognition, we have already developed the kernel gradient matching pursuit method as an approximation technique for kernel-based classification. The conventional kernel gradient matching pursuit method approximates the optimal parameter vector by using a linear combination of a small number of basis vectors. In this paper, we propose an improved kernel gradient matching pursuit method that introduces orthogonality constraints to the obtained basis vector set. We verified the efficiency of the proposed method by conducting recognition experiments based on handwritten image datasets and speech datasets. We realized a scalable kernel optimization that incorporated various models, handled very high-dimensional features (>100 K features), and enabled the use of large scale datasets (> 10 M samples).

本文言語English
ホスト出版物のタイトル2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
ページ1909-1912
ページ数4
DOI
出版ステータスPublished - 2012
外部発表はい
イベント2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
継続期間: 2012 3月 252012 3月 30

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN(印刷版)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
国/地域Japan
CityKyoto
Period12/3/2512/3/30

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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