{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T06:06:36Z","timestamp":1744265196958},"reference-count":29,"publisher":"Elsevier BV","issue":"1-3","license":[{"start":{"date-parts":[[1998,11,1]],"date-time":"1998-11-01T00:00:00Z","timestamp":909878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[1998,11]]},"DOI":"10.1016\/s0925-2312(98)00051-4","type":"journal-article","created":{"date-parts":[[2002,7,25]],"date-time":"2002-07-25T15:25:37Z","timestamp":1027610737000},"page":"81-111","source":"Crossref","is-referenced-by-count":29,"title":["Bayesian Kullback Ying\u2013Yang dependence reduction theory"],"prefix":"10.1016","volume":"22","author":[{"given":"Lei","family":"Xu","sequence":"first","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/S0925-2312(98)00051-4_BIB1","unstructured":"S.-I. Amari, A. Cichocki, H. Yang, A new learning algorithm for blind separation of sources, in: D.S. Touretzky et al. (eds.), Advances in Neural Information Processing 8, MIT Press, Cambridge, MA, 1996, pp. 757\u2013763."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB2","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1162\/neco.1989.1.3.295","article-title":"Unsupervised learning","volume":"1","author":"Barlow","year":"1989","journal-title":"Neural Comput."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB3","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1162\/neco.1995.7.6.1129","article-title":"An information-maximatization approach to blind separation and blind deconvolution","volume":"7","author":"Bell","year":"1995","journal-title":"Neural Comput."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB4","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","article-title":"Independent component analysis \u2013 a new concept?","volume":"36","author":"Comon","year":"1994","journal-title":"Signal Processing"},{"issue":"5","key":"10.1016\/S0925-2312(98)00051-4_BIB5","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1162\/neco.1995.7.5.889","article-title":"The Helmholtz machine","volume":"7","author":"Dayan","year":"1995","journal-title":"Neural Comput."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB6","unstructured":"M. Gaeta, J.-L. Lacounme, Source separation without a priori knowledge: the maximum likelihood solution, in: Proc. European Signal Processing Conf. EUSIPCO90, 1990, pp. 621\u2013624."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB7","unstructured":"C. Jutten, From source separation to independent component analysis: an introduction to special session, in: Proc. 1997 European Symp. on Artificial Neural Networks, Bruges, 16\u201318 April 1997, pp. 243\u2013248."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0893-6080(94)90060-4","article-title":"Representation and separation of signals using nonlinear PCA type Learning","volume":"7","author":"Karhunen","year":"1994","journal-title":"Neural Networks"},{"key":"10.1016\/S0925-2312(98)00051-4_BIB9","unstructured":"I. King, L. Xu, Adaptive contrast enchancement by entropy maximization with a 1-K-1 constrained network, Proc. 1995 Int. Conf. on Neural Information Processing (ICONIP95), vol. II, 30 October\u20133 November, 1995, Beijing, pp. 703\u2013706."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB10","doi-asserted-by":"crossref","unstructured":"J.-P. Nadal, N. Parga, Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer, Networks 5 (1994) 565\u2013581.","DOI":"10.1088\/0954-898X_5_4_008"},{"key":"10.1016\/S0925-2312(98)00051-4_BIB11","unstructured":"B.A. Pearlmutter, L.C. Parra, A context-sensitive generalization of ICA, in: Proc. Int. Conf. on Neural Information Processing (ICONIP 96), Hong Kong, 24\u201327, September 1996, pp. 1235\u20131239."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB12","unstructured":"D.T. Pham, P. Garat, C. Jutten, Separation of a mixture of independent sources through a maximum likelihood approach, in: J. Vandewalle et al. (Eds.), Signal Processing VI: Theories and Applications, Elsevier, Amsterdam, 1992, pp. 771\u2013774."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB13","unstructured":"S. Sharma, Applied Multivariate Techniques. Wiley, New York, 1995."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB14","unstructured":"A. Taleb, C. Jutten, Nonlinearity source separation: the post-nonlinear mixtures, Proc. 1997 European Symp. on Artificial Neural Networks, Bruges, 16\u201318 April 1997, pp. 279\u2013284."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB15","unstructured":"L. Xu, Least MSE reconstruction for self-organization: (I), (II), Proc. 1991 International Joint Conference on Neural Networks(IJCNN91), Singapore, 1991, pp. 2363\u20132373."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB16","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1016\/S0893-6080(05)80107-8","article-title":"Least mean square error reconstruction for self-organizing neural-nets","volume":"6","author":"Xu","year":"1993","journal-title":"Neural Networks"},{"key":"10.1016\/S0925-2312(98)00051-4_BIB17","unstructured":"L. Xu, Beyond PCA learnings: from linear to nonlinear and from global representation to local representation, Invited Talk, Proc. 1994 Int. Conf. on Neural Information Processing, 17\u201320 October, Seuol, Korea, 1994, pp. 943\u2013949."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB18","unstructured":"L. Xu, Theories for unsupervised learning: PCA and its nonlinear extensions, Invited Talk, Proc. 1994 IEEE Int. Conf. on Neural Networks, vol.II, 26 June\u20132 July, Orlando, Florida, pp. 1252\u20131257."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB19","unstructured":"L. Xu, Bayesian Ying\u2013Yang learning based ICA models, Neural Networks for Signal Processing VII: Proc. IEEE Signal Processing Society Workshop, 24\u201326 September, FL, 1997, pp. 476\u2013485."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB20","unstructured":"L. Xu, Bayesian Ying\u2013Yang system and theory as a unified statistical learning approach: (I) unsupervised and semi-unsupervised learning, in: invited paper, S. Amari, N. Kassabov (Eds.), Brainlike Computing and Intelligent Information Systems, Springer, Berlin, 1997, pp. 241\u2013274."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB21","unstructured":"L. Xu, Bayesian Ying\u2013Yang system and theory as a unified stastical learning approach (II): from unsupervised learning to supervised learning and temporal modeling and (III): models and algorithms for dependance reduction, data dimension reduction, ICA and supervised learning, in: K.W. Wong, I. King, D.Y. Yeung (Eds.), Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective (TANC97), Springer, Berlin, 1997, pp. 25\u201360."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB22","unstructured":"L. Xu, BYY dependence reduction theory and blind source separation, Proc. Int. Joint Conf. on Neural Networks, Anchorage, AK, 5\u20139 May 1998, Vol. II, pp. 2495\u20132500."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB23","unstructured":"L. Xu, Bayesian Ying\u2013Yang learning theory for data dimension reduction and determination, J. Comput. Intelligence Finance, Finance Technol. Pub. 1998, 6 (5) pp. 6\u201318."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB24","unstructured":"L. Xu, Bayesian Ying\u2013Yang system and theory as a unified stastical learning approach: (IV) further advances, Proc. Int. Joint Conf. on Neural Networks, Anchorage, AK, 5\u20139 May 1998, Vol. II, pp. 1275\u20131280."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB25","unstructured":"L. Xu, S.-I. Amari, A general independent component analysis framework based on Bayesian\u2013Kullback Ying\u2013Yang Learning, Proc. Int. Conf. on Neural Information Processing (ICONIP 96), Hong Kong, 24\u201327 September, Springer, Singapore, 1996, pp. 1235\u20131239."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB26","unstructured":"L. Xu, C.C. Cheung, S. Amari, Further results on nonlinearity and separation capability of a linear mixture ICA method and learned parametric mixture algorithm, in: C. Fyfe (Ed.), Proc. Int. ICSC Workshop on Independence and Artificial neural networks (I&ANN\u201998), February 9\u201310, Tenerife, Spain, ICSC Academic Press, New York, 1998, pp. 39\u201344."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB27","unstructured":"L. Xu, C.C. Cheung, J. Ruan, S.-I. Amari, Nonlinearity and separation capability: further justification for the ICA algorithm with a learned mixture of parametric densities, Proc. European Symp. on Artificial Neural Networks, Bruges, 16\u201318 April 1997, pp. 291\u2013296."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB28","unstructured":"L. Xu, C.C. Cheung, H.H. Yang, S.-I. Amari, Independent component analysis by the information-theoretic approach with mixture of density, Proc. IEEE Int. Conf. on Neural Networks (IEEE-INNS IJCNN97), vol. III, 9\u201312 June, Houston, TX, USA, 1997, pp. 1821\u20131826."},{"key":"10.1016\/S0925-2312(98)00051-4_BIB29","unstructured":"L. Xu, H.H. Yang, S.-I. Amari, Signal source separation by mixtures accumulative distribution functions or mixture of bell-shape density distribution functions, Research Proposal, presented at FRONTIER FORUM (speakers: D. Sherrington, S. Tanaka, L. Xu, J.F. Cardoso), organized by S. Amari, S. Tanaka, A. Cichocki, The Institute of Physical and Chemical Research (RIKEN), Japan, 10 April, 1996."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231298000514?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231298000514?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,4,27]],"date-time":"2019-04-27T06:29:31Z","timestamp":1556346571000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231298000514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1998,11]]},"references-count":29,"journal-issue":{"issue":"1-3","published-print":{"date-parts":[[1998,11]]}},"alternative-id":["S0925231298000514"],"URL":"https:\/\/doi.org\/10.1016\/s0925-2312(98)00051-4","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[1998,11]]}}}