Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Elements of information theory
Elements of information theory
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
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
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Edgeworth Approximation of Multivariate Differential Entropy
Neural Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The Journal of Machine Learning Research
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Discriminative learning for differing training and test distributions
Proceedings of the 24th international conference on Machine learning
An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression
The Journal of Machine Learning Research
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
Change-Point Detection in Time-Series Data Based on Subspace Identification
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Inlier-Based Outlier Detection via Direct Density Ratio Estimation
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Pool-based active learning in approximate linear regression
Machine Learning
Covariate shift adaptation for semi-supervised speaker identification
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
On the asymptotic properties of a nonparametric L1-test statistic of homogeneity
IEEE Transactions on Information Theory
Density Ratio Estimation: A New Versatile Tool for Machine Learning
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Neural Networks
Sequential change-point detection based on direct density-ratio estimation
Statistical Analysis and Data Mining
Neural Computation
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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.