The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
혼합물 모델링 프레임워크는 많은 응용 프로그램에서 널리 사용됩니다. 이 논문에서 우리는 구성 요소 감소 가우스 혼합 모델을 더 적은 수의 구성 요소를 사용하여 가우스 혼합으로 축소하는 기술입니다. EM(Expectation-Maximization) 알고리즘은 일반적으로 혼합 모델을 데이터에 맞추는 데 사용됩니다. 우리의 알고리즘은 EM 알고리즘을 사용하여 혼합 모델 학습을 확장하여 파생되었습니다. 이 확장에서는 일부 중요한 양을 분석적으로 평가할 수 없다는 사실로 인해 어려움이 발생합니다. 우리는 효과적인 근사를 도입함으로써 이러한 어려움을 극복했습니다. 우리 알고리즘의 효율성은 이를 간단한 합성 구성 요소 감소 작업과 음소 클러스터링 문제에 적용하여 입증됩니다.
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Kumiko MAEBASHI, Nobuo SUEMATSU, Akira HAYASHI, "Component Reduction for Gaussian Mixture Models" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 12, pp. 2846-2853, December 2008, doi: 10.1093/ietisy/e91-d.12.2846.
Abstract: The mixture modeling framework is widely used in many applications. In this paper, we propose a component reduction technique, that collapses a Gaussian mixture model into a Gaussian mixture with fewer components. The EM (Expectation-Maximization) algorithm is usually used to fit a mixture model to data. Our algorithm is derived by extending mixture model learning using the EM-algorithm. In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.12.2846/_p
부
@ARTICLE{e91-d_12_2846,
author={Kumiko MAEBASHI, Nobuo SUEMATSU, Akira HAYASHI, },
journal={IEICE TRANSACTIONS on Information},
title={Component Reduction for Gaussian Mixture Models},
year={2008},
volume={E91-D},
number={12},
pages={2846-2853},
abstract={The mixture modeling framework is widely used in many applications. In this paper, we propose a component reduction technique, that collapses a Gaussian mixture model into a Gaussian mixture with fewer components. The EM (Expectation-Maximization) algorithm is usually used to fit a mixture model to data. Our algorithm is derived by extending mixture model learning using the EM-algorithm. In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem.},
keywords={},
doi={10.1093/ietisy/e91-d.12.2846},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - Component Reduction for Gaussian Mixture Models
T2 - IEICE TRANSACTIONS on Information
SP - 2846
EP - 2853
AU - Kumiko MAEBASHI
AU - Nobuo SUEMATSU
AU - Akira HAYASHI
PY - 2008
DO - 10.1093/ietisy/e91-d.12.2846
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E91-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2008
AB - The mixture modeling framework is widely used in many applications. In this paper, we propose a component reduction technique, that collapses a Gaussian mixture model into a Gaussian mixture with fewer components. The EM (Expectation-Maximization) algorithm is usually used to fit a mixture model to data. Our algorithm is derived by extending mixture model learning using the EM-algorithm. In this extension, a difficulty arises from the fact that some crucial quantities cannot be evaluated analytically. We overcome this difficulty by introducing an effective approximation. The effectiveness of our algorithm is demonstrated by applying it to a simple synthetic component reduction task and a phoneme clustering problem.
ER -