Further advances on Bayesian Ying-Yang harmony learning

After a short tutorial on the fundamentals of Bayes approaches and Bayesian Ying-Yang (BYY) harmony learning, this paper introduces new progresses. A generic information harmonising dynamics of BYY harmony learning is proposed with the help of a Lagrange variety preservation principle, which provide...

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Veröffentlicht in:Applied informatics Jg. 2; H. 1; S. 1 - 45
1. Verfasser: Xu, Lei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 13.06.2015
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Bayesian analysis
Bioinformatics
Computer Applications
Computer Imaging
Computer Science
Factor analysis
Health Informatics
Health Sciences
Machine learning
Manifolds (mathematics)
Mathematical analysis
Matrix methods
Medicine
Pattern Recognition and Graphics
Problem solving
Regression analysis
Search algorithms
Statistics for Life Sciences
Vision
nonGaussian factors
Factor analysis
Binary factors
Lagrange
Multivariate regression
Ying-Yang alternation
Bilinear matrix system
Local factors
Linear mixed model
Automatic model selection
Temporal factors
De-noised Gaussian mixture
Variety preservation
ISSN:2196-0089, 2196-0089
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Zusammenfassung:After a short tutorial on the fundamentals of Bayes approaches and Bayesian Ying-Yang (BYY) harmony learning, this paper introduces new progresses. A generic information harmonising dynamics of BYY harmony learning is proposed with the help of a Lagrange variety preservation principle, which provides Lagrange-like implementations of Ying-Yang alternative nonlocal search for various learning tasks and unifies attention, detection, problem-solving, adaptation, learning and model selection from an information harmonising perspective. In this framework, new algorithms are developed to implement Ying-Yang alternative nonlocal search for learning Gaussian mixture and several typical exemplars of linear matrix system, including factor analysis (FA), mixture of local FA, binary FA, nonGaussian FA, de-noised Gaussian mixture, sparse multivariate regression, temporal FA and temporal binary FA, as well as a generalised bilinear matrix system that covers not only these linear models but also manifold learning, gene regulatory networks and the generalised linear mixed model. These algorithms are featured with a favourable nature of automatic model selection and a unified formulation in performing unsupervised learning and semi-supervised learning. Also, we propose a principle of preserving multiple convex combinations, which leads alternative search algorithms. Finally, we provide a chronological outline of the history of BYY learning studies.
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ISSN:2196-0089
2196-0089
DOI:10.1186/s40535-015-0008-4