报告时间：2015.4.17（星期五）下午14：30
报告地点：博习楼327
报告人：Ivica Kopriva Ph.D
邀请人：陈新建 特聘教授
报告人简介：Ivica Koprivareceived PhD degree from the Faculty of the Electrical Engineering and Computing, University of Zagreb in 1998in the field of signal processing. He has been senior research scientist with the ECE Department, The George Washington University, Washington, DC, USA, 2001-2005. Since 2006, he is a senior scientist at the Ru?er Boškovi? Institute, Zagreb, Croatia. His research is focused on theory and applicationsofinverse problems, most notablyblind source separation,in imaging, spectroscopy and variable selection. He has co-authoredaround 40papers in internationally recognized journals and research monograph:Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised and Unsupervised Learning(Springer-Verlag, 2006). He has received 2009 state award for science of the Republic of Croatia, and 2010,2011and 2012awards of the director of Ru?er Boškovi? Institute for publications inhigh impact factorjournalsand for competitive grant from Croatian Science Foundation. He has been visiting scientist of the Brain Science Institute, RIKEN, Saitama, Japan in October 2011, and visiting professor of the University of South Toulon du Var, La Garde, France, in April 2012. He holdsthreeUSpatents and one Canadianpatent.
报告摘要：The talk will present most recent development in methods for solving sparseness and nonnegativity constrained nonlinear underdetermined blind source separation (uBSS) problem. The methodology is based on mapping original nonlinear uBSS problem onto reproducible kernel Hilbert space and executing sparseness and nonnegativity constrained linear uBSS problem therein. The method will be demonstrated on numerical examples and on demanding experimental problems related to: unsupervised (automated) segmentation of tissues (resp. organs) from multispectral (resp. CT) images, unsupervised extraction of pure components (analytes) from mass spectra of nonlinear chemical reactions as well as on variable (feature) selection in genomics and proteomics.