Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search

DOI10.1016/j.neunet.2010.10.005zbMath1217.68188OpenAlexW2009371655WikidataQ37808029 ScholiaQ37808029MaRDI QIDQ553261

Motoaki Kawanabe, Paul von Bünau, Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama, Makoto Yamada

Publication date: 26 July 2011

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

Full work available at URL: https://doi.org/10.1016/j.neunet.2010.10.005

zbMATH Keywords

dimensionality reductiondensity-ratio estimationunconstrained least-squares importance Fitting

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Optimal \(L_2\)-norm empirical importance weights for the change of probability measure, Density-ratio matching under the Bregman divergence: a unified framework of density-ratio estimation, Least-squares two-sample test, Density-Difference Estimation, Probability density function estimation with the frequency polygon transform, Change-point detection in time-series data by relative density-ratio estimation, Machine learning with squared-loss mutual information, Numerical study of learning algorithms on Stiefel manifold

Uses Software

• UCI-ml
• PRMLT

Cites Work

• Direct importance estimation for covariate shift adaptation
• Semi-supervised speaker identification under covariate shift
• Nonlinear programming and variational inequality problems. A unified approach
• Active learning algorithm using the maximum weighted log-likelihood estimator
• Improving predictive inference under covariate shift by weighting the log-likelihood function
• Semiparametric density estimation under a two-sample density ratio model
• Adaptive importance sampling for value function approximation in off-policy reinforcement learning
• Dimensionality reduction for density ratio estimation in high-dimensional spaces
• Efficient exploration through active learning for value function approximation in reinforcement learning
• Nonparametric and semiparametric models.
• Computational complexity of kernel-based density-ratio estimation: a condition number analysis
• Pool-based active learning in approximate linear regression
• Robust weights and designs for biased regression models: Least squares and generalized \(M\)-estimation
• 10.1162/153244302760185252
• Estimating density ratio with application to discriminant analysis
• Input-dependent estimation of generalization error under covariate shift
• Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression
• Estimating Squared-Loss Mutual Information for Independent Component Analysis
• Kernel Estimators of General Radon-Nikodym Derivatives
• Inferences for case-control and semiparametric two-sample density ratio models
• Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization
• On kernel estimators of density ratio
• Optimal asymptotic quadratic error of kernel estimators of Radon–Nikodym derivatives for strong mixing data
• Elements of Information Theory
• Edgeworth Approximation of Multivariate Differential Entropy
• Theory of Reproducing Kernels
• On Information and Sufficiency
• A new look at the statistical model identification
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item