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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Estimation du rapport de deux fonctions de densité de probabilité (c'est-à-dire le importance) a récemment attiré beaucoup d'attention puisque les estimateurs d'importance peuvent être utilisés pour résoudre divers problèmes d'apprentissage automatique et d'exploration de données. Dans cet article, nous proposons une nouvelle méthode d’estimation de l’importance utilisant un mélange d'analyseurs probabilistes des composantes principales. La méthode proposée est plus flexible que les approches existantes et devrait bien fonctionner lorsque la fonction d'importance cible est corrélée et déficiente en termes de rang. A travers des expérimentations, nous illustrons la validité de l'approche proposée.
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Makoto YAMADA, Masashi SUGIYAMA, Gordon WICHERN, Jaak SIMM, "Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2846-2849, October 2010, doi: 10.1587/transinf.E93.D.2846.
Abstract: Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2846/_p
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@ARTICLE{e93-d_10_2846,
author={Makoto YAMADA, Masashi SUGIYAMA, Gordon WICHERN, Jaak SIMM, },
journal={IEICE TRANSACTIONS on Information},
title={Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers},
year={2010},
volume={E93-D},
number={10},
pages={2846-2849},
abstract={Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.},
keywords={},
doi={10.1587/transinf.E93.D.2846},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
T2 - IEICE TRANSACTIONS on Information
SP - 2846
EP - 2849
AU - Makoto YAMADA
AU - Masashi SUGIYAMA
AU - Gordon WICHERN
AU - Jaak SIMM
PY - 2010
DO - 10.1587/transinf.E93.D.2846
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.
ER -