Bounded Component Analysis: An Algorithmic Framework for Blind Separation of Independent and Dependent Components
Bounded Component Analysis (BCA) is a recently introduced framework which can be considered as an extension of Independent Component Analysis (ICA) for bounded magnitude signals. In BCA, the boundedness of signals is exploited to replace independence assumption with a weaker ‘‘domain separability’’ assumption. As a result BCA algorithms can be used to separate dependent as well as independent signals from their mixtures. In this talk, I'll introduce a geometric approach for developing instantaneous and convolutive BCA algorithms. Furthermore, the potential benefits of the corresponding BCA algorithms are illustrated through different application examples.
This is a joint work with Huseyin A. Inan.
Alper T, Erdogan received his B.S.(93) in EE from Middle East Technical University in Turkey, M.S. (95) and Ph.D.(99) in EE from Stanford University. He worked as a principal research engineer in Globespan-Virata (formerly Excess Bandwidth) in Santa Clara CA during 1999-2001. In 2002, he joined Electrical-Electronics Engineering Department of Koc University in Istanbul, Turkey where he is currently an associate professor. Dr. Erdogan served as an associate editor for IEEE Transactions on Signal Processing and as a member of IEEE Signal Processing Theory and Methods Technical Committee. He is a recipient of TUBITAK Encouragement Award, Werner Von Siemens Award and Turkish Academy of Sciences Outstanding Young Scholar Award