Bayesian Ying-Yang
System and Harmony Learning Theory
Major Readings
- Lei Xu (2015), “Further advances on Bayesian Ying-Yang harmony
learning”, Springer-Nature,OA Journal, Applied
Informatics 2015, 2:5 doi:10.1186/s40535-015-0008-4.
- Lei Xu (2012), " On essential topics of
BYY harmony learning: Current status, challenging issues, and gene
analysis applications ", A special issue on Machine
learning and intelligence science: IScIDE (C),
Journal of Frontiers of Electrical and Electronic Engineering 7(1) (2012)
147-196.
- Lei Xu (2011), "Another
perspective of BYY harmony learning: representation in multiple layers,
co-decomposition of data covariance matrices, and applications to network
biology. A special issue on Machine learning and intelligence science:
IScIDE2010 (A), Journal of Frontiers of Electrical and Electronic
Engineering in China 6(1) (2011) 86-119.
- Shikui TU, Lei Xu (2011), "An investigation of several
typical model selection criteria for detecting the number of signals",
A special issue on Machine learning and
intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical
and Electronic Engineering in China 6(2) (2011) 245-255.
·
Shikui TU, Lei Xu (2011), "Parameterizations make different model
selections: Empirical findings from factor analysis", A special issue on Machine learning and
intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical and
Electronic Engineering in China 6(2) (2011) 256-274.
·
Lei SHI, Shikui TU, Lei Xu (2011), " Learning Gaussian mixture with automatic model selection:A comparative study on
three Bayesian related approaches", A special issue on Machine
learning and intelligence science: IScIDE2010 (B), Journal of Frontiers of
Electrical and Electronic Engineering in China 6(2) (2011) 215-244.
- Penghui WANG, Lei
SHI, Lan DU, Hongwei LIU, Lei Xu , Zheng BAO, (2011),
"Radar
HRRP statistical recognition with temporal factor analysis by automatic
Bayesian Ying-Yang harmony learning ",
A special issue on Machine learning and
intelligence science: IScIDE2010 (B), Journal of Frontiers of Electrical
and Electronic Engineering in China 6(2) (2011) 300-317.
- Shi L, Wang P., Liu H., Lei Xu , and Bao
Z(2011), Radar HRRP Statistical Recognition With
Local Factor Analysis by Automatic Bayesian Ying-Yang Harmony Learning,
IEEE Trans. Signal Process., 2011, 59(2):610-617.
- Lei Xu (2010),
"Bayesian Ying-Yang
system, best harmony learning, and five action circling", A special
issue on Emerging Themes on Information Theory and Bayesian Approach,
Journal of Frontiers of Electrical and Electronic Engineering in China,
5(3):281-328, 2010.
- Lei Xu (2010), "Machine learning problems from
optimization perspective", A special issue for CDGO 07, Journal of Global Optimization, 47, 2010,
369-401.
- Lei Xu (2009), "Learning Algorithms
for RBF Functions and Subspace Based Functions", Ch.3 in
"Handbook of Research on Machine Learning Applications and Trends:
Algorithms, Methods and Techniques", eds. by Olivas, Guerrero, Sober,
Benedito, & Lopez, IGI Global publication,
pp60-94.
- Lei Xu (2008), `` Bayesian Ying Yang
System, Best Harmony Learning, and Gaussian Manifold Based Family", In J.M. Zurada et al. (Eds.)
Computational Intelligence: Research Frontiers, WCCI2008 Plenary/Invited
Lectures, LNCS5050,
48-78,2008.
·
Lei Xu (2007), Bayesian Ying Yang Learning,
In Scholarpedia, no.18395, http://scholarpedia.org,
2007.
- Lei Xu (2007), `` A unified perspective
and new results on RHT computing, mixture based learning, and multi-learner
based problem solving ", Pattern
Recognition, (40) 2129-2153.
- Lei Xu (2007), `` Trends on Regularization and
Model Selection in Statistical Learning: A Perspective from Bayesian Ying
Yang Learning ", Studies in Computational Intelligence, 63, pp. 365-406,
2007.
- Lei Xu (2005),
``Fundamentals, Challenges, and Advances of Statistical Learning for
Knowledge Discovery and Problem Solving: A BYY Harmony Perspective",
Proceedings of International Conference on Neural Networks and Brain,
Keynote talk, Vol. 1, pp. 24-55, Oct. 13-15, Beijing, China, 2005.
- Lei Xu (2004), ``Temporal BYY
Encoding, Markovian State Spaces, and Space Dimension Determination",
IEEE Trans on Neural Networks, Vol. 15, No. 5, pp1276-1295, 2004.
- Lei Xu (2004), ``Advances on BYY
Harmony Learning: Information Theoretic Perspective, Generalized
Projection Geometry, and Independent Factor Auto-determination", IEEE
Trans on Neural Networks, Vol. 15, No. 4, pp885-902, 2004.
- Lei Xu (2004),
``Bayesian Ying Yang Learning (I): A Unified Perspective for Statistical
Modeling", Intelligent Technologies for Information Analysis, N. Zhong and J. Liu (eds),
Springer, pp615-659, 2004.
- Lei Xu (2004), ``Bayesian
Ying Yang Learning (II): A New Mechanism for Model Selection and
Regularization", Intelligent Technologies for Information Analysis,
N. Zhong and J. Liu (eds),
Springer, pp661-706, 2004.
- Lei Xu (2003), `` BYY learning,
regularized implementation, and model selection on modular networks with
one hidden layer of binary units ", Neurocomputing,
Vol. 51, pp 277-301, 2003. Errata
to this paper is given here , which is published on Neurocomputing, Vol. 55, pp 405-406, 2003.
- Lei Xu (2002a), `` Ying-Yang
learning", The Handbook of Brain Theory and Neural Networks, 2nd ed.,
Michael A. Arbib, The MIT Press, pp1231-1237,
2002.
- Lei Xu (2002b), ``BYY harmony
learning, structural RPCL, and topological self-organizing on mixture
models", Neural Networks, Vol. 15, pp1125-1151, 2002.
- Lei Xu (2001a), ``BYY Harmony
Learning, Independent State Space and Generalized APT Financial
Analyses", IEEE Trans. on Neural Networks, Vol. 12, No.4, pp822-849,
July, 2001. An Errata to
this paper is given on IEEE Trans. on Neural Networks, Vol. 13, No.4,
1023, July, 2002.
- Lei Xu (2001b), ``Best Harmony,
Unified RPCL and Automated Model Selection for Unsupervised and Supervised
Learning on Gaussian Mixtures, ME-RBF Models and Three-Layer Nets ",
International Journal of Neural Systems, Vol.11, No.1, pp3-69, 2001.
- Lei Xu (2000a), `` Temporal BYY
Learning for State Space Approach, Hidden Markov Model and Blind Source
Separation ", IEEE Trans on Signal Processing, Vol. 48, No. 7,
2132-2144, July, 2000.
- Lei Xu (2000b), `` BYY Learning
System and Theory for Parameter Estimation, Data Smoothing Based
Regularization and Model Selection ", Neural, Parallel and Scientific
Computations, Vol. 8, pp55-82, 2000.
- Lei Xu (1998a), ``RBF Nets,
Mixture Experts, and Bayesian Ying-Yang Learning", Neurocomputing, Vol. 19, No.1-3, pp223-257, 1998.
- Lei Xu(1998b), ``Bayesian Kullback Ying-Yang Dependence Reduction Theory ",
Neurocomputing, Vol.22, No.1-3, a special issue
on Independence and artificial neural networks, pp81-112, 1998.
- Lei Xu(1998c), ``Bayesian Ying-Yang
Dimension Reduction and Determination", Journal of Computational
Intelligence in Finance, Vol.6, No.5, a special issue on Complexity and
Dimensionality Reduction in Finance. pp6-18, 1998.
- Lei Xu (1997), ``Bayesian Ying-Yang
Machine, Clustering and Number of Clusters", Pattern Recognition
Letters, Vol.18, No.11-13, pp1167-1178, 1997.
- Lei Xu (1997), ``New Advances
on Bayesian Ying-Yang Learning SystemWith Kullback and Non-Kullback
Separation Functionals", Proceedings of
1997 IEEE-(INNS) Conference on Neural Networks, Houston, TX, June. 9-12,
Vol. 3, pp1942-1947, 1997
- Lei Xu (1997), `` Bayesian
Ying-Yang System and Theory as A Unified Statistical Learning Approach
(III): Models and Algorithms for Dependence Reduction, Data Dimension
Reduction, ICA and Supervised Learning", Invited paper, K.W.Wong, I. King and D.Y.Yeung eds, Theoretical
Aspects of Neural Computation: A Multidisciplinary Perspective (TANC97),
Springer-Verlag, pp43-60, 1997.
- Lei Xu (1997), ``Bayesian
Ying-Yang System and Theory as A Unified Statistical Learning Approach:
(II) From Unsupervised Learning to Supervised Learning and Temporal
Modeling ", Invited paper, K.W.Wong,
I. King and D.Y.Yeung eds,
Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective
(TANC97), Springer-Verlag, pp25-42, 1997.
- Lei Xu (1997), ``Bayesian
Ying-Yang System and Theory: An Unified Approach
for Statistical Learning: (I) Unsupervised and Semi-Unsupervised
Learning", Invited paper, S. Amari and N. Kassabov
eds., Brain-like Computing and Intelligent Information Systems, Springer-Verlag, pp241-274, 1997.
- Lei Xu (1995),``A Unified Learning Scheme: Bayesian-Kullback YING-YANG Machine", Advances in Neural Information Processing Systems 8: Proceedings of
the 1995 Conference, eds., David S.
Touretzky, Michael Mozer,
Michael Hasselmo, MIT Press, Cambridge MA,
pp444-450.
- Lei Xu (1995),
`` Bayesian-Kullback Coupled YING-YANG Machines: Unified Learnings and New
Results on Vector Quantization", Proceedings of International
Conference on Neural Information Processing, Keynote Speaker, Oct
30-Nov.3, Beijing, China, 1995, pp977-988.
Other Readings
· Long, W., Tu, S., & Xu L.
(2017). A Comparative Study on Lagrange
Ying-Yang Alternation Method in Gaussian Mixture-Based Clustering. Lecture Notes in Computer Science, Vol 10585:In International Conference on Intelligent Data
Engineering and Automated Learning (pp. 489-499). Springer, Cham.
·
Guangyong Chen, Fengyuan Zhu, Pheng Ann Heng, Lei Xu (2015), “Image denoising, local factor analysis, Bayesian
Ying-Yang harmony learning”, in
Advances in Independent Component Analysis and Learning Machines, (Bingham, Kaski, Lampinen and Laaksonen, Eds), Elsevier,
Amsterdam, 2015.
·
Guangyong Chen, Pheng Ann Heng, Lei Xu (2014), “Projection-embedded
BYY learning algorithm for Gaussian mixture-based clustering”, Springer-Nature OA Journal, Applied Informatics 2015, 2:4
doi:10.1186/s40535-015-0007-5.
· Shikui Tu, Lei Xu (2014),
“Learning
Binary Factor Analysis with Automatic Model Selection”,
Neurocomputing 134 (2014) 149-158.
·
Zaihu Pang, Shikui Tu, Xihong
Wu, Lei Xu
(2013), “Discriminative
GMM-HMM Acoustic Model Selection Using Two-Level Bayesian Ying-Yang Harmony
Learning”, Lecture Notes in Computer Science: Intelligent Science and Intelligent
Data Engineering, Vol .7751, pp. 719-726, 2013.
· Lei Xu (2012), “Semi-Blind Bilinear Matrix System, BYY Harmony Learning,
and Gene Analysis Applications,” Proc. of 6th International Conf. on New Trends in
Information Science, Service Science and Data Mining, pp. 661-666, Oct. 23 -
25, 2012, Taipei.
· Shikui Tu, Dingsheng Luo, Runsheng Chen and Lei Xu (2012), “A Non-Gaussian Factor Analysis Approachto
Transcription Network Component Analysis,” IEEE Symposium on Computational Intelligence in
Bioinformatics and Computational Biology (CIBCB 2012), pp. 404-411,
May 9-12, San Diego, California.
· Tu S, Chen R, Lei Xu (2011), A binary matrix
factorization algorithm for protein complex prediction. Proteome Science 2011, 9 (Suppl
1): S18, 2011.
· Shi L, Wang P., Liu H., Lei Xu , and Bao Z(2011), Radar HRRP Statistical Recognition With Local
Factor Analysis by Automatic Bayesian Ying-Yang Harmony Learning, IEEE Trans.
Signal Process., 2011, 59(2):610–617.
· Shi, L, Tu
S, Lei Xu, Gene clustering by
structural prior based local factor analysis model under Bayesian Ying-Yang
harmony learning. In: Proceedings of the BIBM 2010 International on
Bioinformatics and Biomedicine, Hong Kong, December 18–21, 2010, pp 696-699.
· Tu S, Chen R, Lei Xu,. A binary matrix factorization algorithm for protein complex prediction.
In: Proceedings of the BIBM 2010 International Workshop on Computational
Proteomics, Hong Kong, December 18–21, 2010, pp 113-118.
· An, Y.J., Hu, X.L., and Lei Xu (2006), ``A Comparative Investigation on Model
Selection in Independent Factor Analysis" Journal of Mathematical Modeling
and Algorithms 5, pp.447-473.
·
X. Hu and Lei Xu (2004), ``A Comparative Study on Selection of Cluster
Number and Local Subspace Dimension in the Mixture PCA Models", Advances
in Neural Networks -ISNN 2006:
Lecture Notes in Computer Sciences, Vol. 3971, pp. 1214 - 1221, Springer Verlag, 2006.
· X. Hu and
Lei Xu (2004), ``A comparative
investigation on subspace dimension determination", Neural Networks, Vol.
17, pp1051-1059, 2004,
· X. Hu and
Lei Xu (2004), ``Investigation on
Several Model Selection Criteria for Determining the Number of Cluster",
Neural Information Processing - Letters and Reviews, Vol. 4, No. 1, pp1-10,
July 2004,
· Ma, J,
Wang, T, and Lei Xu (2004), ``A
gradient BYY harmony learning rule on Gaussian mixture with automated model
selection", Neurocomputing, Vol 56, 481 - 487,
2004.
· Yiu-ming Cheung and Lei Xu (2003), `` Further studies on
temporal factor analysis: comparison and Kalman
Filter-based algorithm ", Neurocomputing, Vol.
50, 2003, 87-103.
· Ouyang
Ning, Wing-kai Lam, K. Yamauchi and Lei Xu (1999),
``Using An Improved Back Propagation Learning Method
to Diagnose The Sites of Cardiac Hypertrophy", MD Computing, Vol. 16,
No.1, pp79-81, 1999.