A Density-ratio Framework for Statistical Data Processing

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概要

• 論文の詳細を見る

• In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea — known as Vapnik's principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a lot of attention in the machine learning and data mining communities. The purpose of this paper is to introduce to the computer vision community recent advances in density ratio estimation methods and their usage in various statistical data processing tasks such as non-stationarity adaptation, outlier detection, feature selection, and independent component analysis.

• 一般社団法人 情報処理学会の論文

著者

• Takeuchi Ichiro

  Nagoya Institute of Technology

• Sugiyama Masashi

  Tokyo Inst. Of Technol.

• KANAMORI Takafumi

  Nagoya University

• Suzuki Taiji

  The Univ. Of Tokyo

• Hido Shohei

  IBM Research

• Sese Jun

  Ochanomizu University

• Wang Liwei

  Peking University, Beijing

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