Dimensionality reduction for density ratio estimation in high-dimensional spaces

DOI10.1016/j.neunet.2009.07.007zbMath1401.62097OpenAlexW2119410311WikidataQ44528641 ScholiaQ44528641MaRDI QIDQ1784536

Masashi Sugiyama, Motoaki Kawanabe, Pui Ling Chui

Publication date: 27 September 2018

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.07.007

zbMATH Keywords

dimensionality reductiondensity ratio estimationlocal Fisher discriminant analysisunconstrained least-squares importance fitting

Mathematics Subject Classification ID

Density estimation (62G07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Necessary and sufficient conditions of proper estimators based on self density ratio for unnormalized statistical models, Least-squares two-sample test, Density-Difference Estimation, Probability density function estimation with the frequency polygon transform, Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search, Change-point detection in time-series data by relative density-ratio estimation, Machine learning with squared-loss mutual information, Sequential change‐point detection based on direct density‐ratio estimation

Uses Software

• FastICA
• LOF
• PRMLT

Cites Work

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