On essential topics of BYY harmony learning： Current status, challenging issues, and gene analysis applications  

作　　者：Lei XU 

机构地区：[1]Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China

出　　处：《Frontiers of Electrical and Electronic Engineering in China》2012年第1期147-196,共50页中国电气与电子工程前沿（英文版）

摘　　要：As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.

关 键 词：Bayesian Ying-Yang （BYY） harmonylearning harmony functional automatic model selec-tion Gaussian mixture hidden Markov model （HMM）gated temporal factor analysis hierarchical Gaussianmixture manifold learning semi-supervised learning semi-blind learning genome-wide association exome se-quencing analysis gene transcriptional regulation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] Q78[自动化与计算机技术—控制科学与工程]