The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method--which we call the Gaussian mixture KLIEP (GM-KLIEP)--is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copy
Makoto YAMADA, Masashi SUGIYAMA, "Direct Importance Estimation with Gaussian Mixture Models" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 10, pp. 2159-2162, October 2009, doi: 10.1587/transinf.E92.D.2159.
Abstract: The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method--which we call the Gaussian mixture KLIEP (GM-KLIEP)--is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2159/_p
Copy
@ARTICLE{e92-d_10_2159,
author={Makoto YAMADA, Masashi SUGIYAMA, },
journal={IEICE TRANSACTIONS on Information},
title={Direct Importance Estimation with Gaussian Mixture Models},
year={2009},
volume={E92-D},
number={10},
pages={2159-2162},
abstract={The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method--which we call the Gaussian mixture KLIEP (GM-KLIEP)--is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.},
keywords={},
doi={10.1587/transinf.E92.D.2159},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Direct Importance Estimation with Gaussian Mixture Models
T2 - IEICE TRANSACTIONS on Information
SP - 2159
EP - 2162
AU - Makoto YAMADA
AU - Masashi SUGIYAMA
PY - 2009
DO - 10.1587/transinf.E92.D.2159
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2009
AB - The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method--which we call the Gaussian mixture KLIEP (GM-KLIEP)--is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
ER -