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Learning local factor analysis versus mixture of factor analyzers with automatic model selection

文献类型：期刊论文

作者Shi, Lei1; Liu, Zhi-Yong2; Tu, Shikui1; Xu, Lei1
刊名NEUROCOMPUTING
出版日期2014-09-02
卷号139页码:3-14
关键词Automatic model selection Mixture of factor analyzers Local factor analysis Variational Bayes Bayesian Ying-Yang Dirichlet-Normal-Gamma
英文摘要Considering Factor Analysis (FA) for each component of Gaussian Mixture Model (GMM), clustering and local dimensionality reduction can be addressed simultaneously by Mixture of Factor Analyzers (MFA) and Local Factor Analysis (LFA), which correspond to two FA parameterizations, respectively. This paper investigates the performance of Variational Bayes (VB) and Bayesian Ying-Yang (BYY) harmony learning on MFA/LFA for the problem of automatically determining the component number and the local hidden dimensionalities (i.e., the number of factors of FA in each component). Similar to the existing VB learning algorithm on MFA, we develop an alternative VB algorithm on LFA with a similar conjugate Dirichlet-Normal-Gamma (DNG) prior on all parameters of LFA. Also, the corresponding BYY algorithms are developed for MFA and LFA. A wide range of synthetic experiments shows that LFA is superior to MFA in model selection under either VB or BYY, while BYY outperforms VB reliably on both MFA and LFA. These empirical findings are consistently observed from real applications on not only face and handwritten digit images clustering, but also unsupervised image segmentation. (C) 2014 Elsevier B.V. All rights reserved.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence
研究领域[WOS]Computer Science
关键词[WOS]IMAGE SEGMENTATION ; EM ALGORITHM
收录类别SCI
语种英语
WOS记录号WOS:000337661800002
源URL[http://ir.ia.ac.cn/handle/173211/3037]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位1.Chinese Univ Hong Kong, Dept Comp Sci & Engn, Shatin, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Shi, Lei,Liu, Zhi-Yong,Tu, Shikui,et al. Learning local factor analysis versus mixture of factor analyzers with automatic model selection[J]. NEUROCOMPUTING,2014,139:3-14.
APA Shi, Lei,Liu, Zhi-Yong,Tu, Shikui,&Xu, Lei.(2014).Learning local factor analysis versus mixture of factor analyzers with automatic model selection.NEUROCOMPUTING,139,3-14.
MLA Shi, Lei,et al."Learning local factor analysis versus mixture of factor analyzers with automatic model selection".NEUROCOMPUTING 139(2014):3-14.

入库方式： OAI收割

来源：自动化研究所

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