Speaker: Prof. Alex Dimakis
Affiliation: University of Texas, Austin
Principal Component Analysis (PCA) is arguably the workhorse of high dimensional data analysis (one of the most widely used algorithms with applications ranging from computer vision, document clustering to network anomaly detection). I am going to talk about PCA variants like Sparse PCA and the recently developed PathPCA that offers higher data interpretability. I will discuss theoretical challenges and some novel algorithmic results for obtaining principal components supported on graph structures. For several datasets, we obtain excellent empirical performance and provable upper bounds that guarantee that our objective is close to the unknown optimum. (Based on joint works with M. Asteris, A. Kyrillidis, D. Papailiopoulos, H. Yi, and B. Chandrasekaran.)
Alex Dimakis is an Associate Professor at the Electrical and Computer Engineering Department, University of Texas at Austin. From 2009 until 2012, he was with the Viterbi School of Engineering, University of Southern California. He received his Ph.D. in 2008 and an M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009, he was a CMI postdoctoral scholar at Caltech.
He received an NSF Career Award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. His research interests include information theory, coding theory, and machine learning.
For more information contact Professor van der Schaar ()
Date(s) - Nov 09, 2015
1:00 pm - 2:00 pm
EE-IV Shannon Room #54-134
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095