Elements of information theory
Elements of information theory
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An introduction to variable and feature selection
The Journal of Machine Learning Research
Learning and evaluating classifiers under sample selection bias
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Edgeworth Approximation of Multivariate Differential Entropy
Neural Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Nonparametric Quantile Estimation
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Multi-task learning for HIV therapy screening
Proceedings of the 25th international conference on Machine learning
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
Inlier-Based Outlier Detection via Direct Density Ratio Estimation
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Estimating Squared-Loss Mutual Information for Independent Component Analysis
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Covariate shift adaptation for semi-supervised speaker identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Efficient Sample Reuse in EM-Based Policy Search
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Adaptive importance sampling with automatic model selection in value function approximation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Mutual information approximation via maximum likelihood estimation of density ratio
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
A Least-squares Approach to Direct Importance Estimation
The Journal of Machine Learning Research
Semi-supervised speaker identification under covariate shift
Signal Processing
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A new general framework of statistical data processing based on the ratio of probability densities has been proposed recently and gathers a great deal of attention in the machine learning and data mining communities [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. This density ratio framework includes various statistical data processing tasks such as non-stationarity adaptation [18,1,2,4,13], outlier detection [19,20,21,6], and conditional density estimation [22,23,24,15]. Furthermore, mutual information--which plays a central role in information theory [25]--can also be estimated via density ratio estimation. Since mutual information is a measure of statistical independence between random variables [26,27,28], density ratio estimation can be used also for variable selection [29,7,11], dimensionality reduction [30,16], and independent component analysis [31,12].