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Role of Statistical Diversity in Single and Multi-set Source Separation: Theory and Applications in Medical Imaging
March 16, 2016 @ 2:00 pm - 3:00 pm
ABSTRACT: Data-drivenmethodsarebasedonasimplegenerativemodelandhencecanminimize the underlying assumptions on the data. They have emerged as promising alternatives to the traditional
model-based approaches in applications where the unknown dynamics are hard to characterize. Independent component analysis (ICA), in particular, has been a popular data-driven approach and an active area of research. Starting from a simple linear mixing model and the assumption of statistical independence, ICA can recover a set of linearly-mixed components to within 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.
ICA can be achieved using different types of diversity—statistical property—and as demonstrated in this talk, can be posed to simultaneously account for multiple types of diversity such as higher-order-statistics, sample dependence, non-circularity, and nonstationarity. A recent generalization of ICA, independent vector analysis (IVA), generalizes ICA to multiple data sets and adds the use of one more type of diversity, statistical dependence across the data sets, for jointly achieving independent decomposition of multiple data sets. With the addition of each new diversity type, identification of a broader class of signals become possible, and in the case of IVA, this includes sources that are independent and identically distributed Gaussians.
This talk reviews the fundamentals and properties of ICA and IVA when multiple types of diversity are taken into account, highlights the connections between the two, especially in the way both approaches make use of signal diversity. A number of examples are given to demonstrate the successful application of ICA and IVA to the analysis and fusion of medical imaging data, emphasizing the role of diversity in these applications.
Biography: Tülay Adalire ceived the Ph.D. degree in Electrical Engineering from North Carolina State University, Raleigh, NC, USA, in 1992 and joined the faculty at the University of Maryland Baltimore County (UMBC), Baltimore, MD, the same year.
She is currently a Distinguished University Professor in the Department of Computer Science and Electrical Engineering at UMBC.She has been very active in conference and workshop organizations. She was the general or technical co-chair of the IEEE Machine Learning for Signal Processing (MLSP) and Neural Networks for Signal Processing Workshops 2001−2008, and helped organize a number of conferences including the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). She has served or currently serving on numerous editorial boards and technical committees of the IEEE Signal Processing Society. She was the chair of the technical committee on MLSP, 2003−2005 and 2011−2013.
Prof. Adali is a Fellow of the IEEE and the AIMBE, a Fulbright Scholar, recipient of a 2010 IEEE Signal Processing Society Best Paper Award, 2013 University System of Maryland Regents’ Award for Research, and an NSF CAREER Award.
She was an IEEE Signal Processing Society Distinguished Lecturer for 2012 and 2013. Her research interests are in the areas of statistical signal processing, machine learning for signal processing, and biomedical data analysis.
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