Subspace pursuit method for kernel-log-linear models

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

*この研究の対応する著者

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, log-linear models without mixtures have been used as emission probability density functions in hidden Markov models for automatic speech recognition. In that framework, nonlinearly-transformed high-dimensional features are used to achieve the nonlinear classification of the original observation vectors without using mixtures. In this paper, with the goal of using high-dimensional features in kernel spaces, the cutting plane subspace pursuit method proposed for support vector machines is generalized and applied to log-linear models. The experimental results show that the proposed method achieved an efficient approximation of the feature space by using a limited number of basis vectors.

本文言語English
ホスト出版物のタイトル2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
ページ4500-4503
ページ数4
DOI
出版ステータスPublished - 2011
イベント36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
継続期間: 2011 5月 222011 5月 27

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

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
国/地域Czech Republic
CityPrague
Period11/5/2211/5/27

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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