ICA and IVA: Theory, Connections, and Applications
Tulay Adali (University of Maryland, Baltimore County)


Data-driven methods are based on a simple generative model and hence can minimize the assumptions on the nature of data. They have emerged as promising alternatives to the traditional model-based approaches in many applications where the underlying dynamics are hard to characterize. Independent component analysis (ICA), in particular, has been a popular data-driven approach and an active area of research. By starting from a simple linear mixing model and imposing the constraint of statistical independence on the underlying components, ICA can recover the linearly mixed components subject to only a scaling and permutation ambiguity. It has been successfully applied to numerous data analysis problems in areas as diverse as biomedicine, communications, finance, geophysics, and remote sensing.

This talk reviews the fundamentals and properties of ICA, in particular, for the most widely used approach to achieve ICA, mutual information minimization, introduces the generalization of ICA for analysis of multiple datasets, independent vector analysis (IVA), and discusses the connections between ICA and IVA. Two key problems for achieving a successful ICA decomposition, matrix optimization and density matching, are discussed in detail, along with new powerful approaches for addressing these two key signal processing problems. Successful application examples in medical image analysis and speech processing are presented, and important practical issues are emphasized.


Tülay Adali (F) received the Ph.D. degree in electrical engineering from North Carolina State University, Raleigh, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, the same year where she currently is a Professor in the Department of Computer Science and Electrical Engineering. She has held visiting positions at École Supérieure de Physique et de Chimie Industrielles, Paris, France; Technical University of Denmark, Lyngby, Denmark; Katholieke Universiteit, Leuven, Belgium; University of Campinas, Brazil; and University of Newcastle, Australia.

Prof. Adali assisted in the organization of a number of international conferences and workshops including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), the IEEE International Workshop on Neural Networks for Signal Processing (NNSP), and the IEEE International Workshop on Machine Learning for Signal Processing (MLSP). She was the General Co-Chair, NNSP (2001–2003); Technical Chair, MLSP (2004–2008); Program Co-Chair, MLSP (2008 and 2009), 2009 International Conference on Independent Component Analysis and Source Separation; Publicity Chair, ICASSP (2000 and 2005); and Publications Co-Chair, ICASSP 2008.

Prof. Adali chaired the IEEE SPS Machine Learning for Signal Processing Technical Committee (2003–2005); Member, SPS Conference Board (1998–2006); Member, Bio Imaging and Signal Processing Technical Committee (2004–2007); and Associate Editor, IEEE Transactions on Signal Processing (2003–2006), Elsevier Signal Processing Journal (2007–2010). She is currently Chair of the MLSP Technical Committee and serving on the Signal Processing Theory and Methods Technical Committee; Associate Editor, IEEE Transactions on Biomedical Engineering and Journal of Signal Processing Systems for Signal, Image, and Video Technology; Senior Editorial Board member, IEEE Journal of Selected Areas in Signal Processing.

Prof. Adali is a Fellow of the IEEE and the AIMBE, and the recipient of a 2010 IEEE Signal Processing Society Best Paper Award and an NSF CAREER Award. Prof. Adali’s research interests are in the areas of statistical signal processing, machine learning for signal processing, and biomedical data analysis.