文章基本信息

标题：A Density-ratio Framework for Statistical Data Processing
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作者：Masashi Sugiyama ; Takafumi Kanamori ; Taiji Suzuki 等
期刊名称：Information and Media Technologies
电子版ISSN：1881-0896
出版年度：2009
卷号：4
期号：4
页码：962-987
DOI：10.11185/imt.4.962
出版社：Information and Media Technologies Editorial Board
摘要：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.