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

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Learning local factor analysis versus mixture of factor analyzers with automatic model selection
	Shi, Lei 1; Liu, Zhi-Yong2 ; Tu, Shikui 1; Xu, Lei 1
发表期刊	NEUROCOMPUTING
	2014-09-02
卷号	139 页码:3-14
文章类型	Article
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.
关键词	Automatic Model Selection Mixture Of Factor Analyzers Local Factor Analysis Variational Bayes Bayesian Ying-yang Dirichlet-normal-gamma
WOS标题词	Science & Technology ; Technology
关键词[WOS]	IMAGE SEGMENTATION ; EM ALGORITHM
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000337661800002
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3037
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
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.