کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404527 677432 2010 16 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dimensionality reduction for density ratio estimation in high-dimensional spaces
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Dimensionality reduction for density ratio estimation in high-dimensional spaces
چکیده انگلیسی

The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various data processing tasks such as non-stationarity adaptation, outlier detection, and feature selection. Recently, several methods have been developed for directly estimating the density ratio without going through density estimation and were shown to work well in various practical problems. However, these methods still perform rather poorly when the dimensionality of the data domain is high. In this paper, we propose to incorporate a dimensionality reduction scheme into a density-ratio estimation procedure and experimentally show that the estimation accuracy in high-dimensional cases can be improved.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 23, Issue 1, January 2010, Pages 44–59
نویسندگان
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