Event Location: NSH 1507
Bio: Aleix M. Martinez is a Professor in the Department of Electrical and Computer Engineering at The Ohio State University (OSU), where he is the founder and director of the Computational Biology and Cognitive Science Lab. He is also affiliated with the Department of Biomedical Engineering and to the Center for Cognitive and Brain Sciences, where he is a member of the executive committee. Aleix has served as an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Affective Computing, Image and Vision Computing, and Computer Vision and Image Understanding. He has been an area chair for many top conferences, including CVPR, ICCV and FG, and was a Program co-Chair for CVPR 2014. He is also a member of NIH’s Cognition and Perception study section. His new scientific goal is to understand why Columbus is colder than Pittsburgh.

Abstract: The Bayes criterion is generally regarded as the holy grail in classification because, for known distributions, it leads to the smallest possible classification error. Unfortunately, the Bayes classification boundary is generally nonlinear and its associated error can only be calculated under unrealistic assumptions. In this talk, we will show how these obstacles can be readily and efficiently averted yielding Bayes optimal algorithms in machine learning, statistics, computer vision and other areas of scientific inquiry. In this journey, we will extend the notion of homoscedasticity (meaning of the same variance) to spherical-homoscedasticity (meaning of the same variance up to a rotation) and show how this allows us to generalize the Bayes criterion under more realistic assumptions. This will lead to a new concept of kernel mappings with applications in classification (machine learning), shape analysis (statistics), structure from motion (computer vision), and others. We will then define other optimization criteria where Bayes cannot be readily applied and define the use of kernels in labeled graphs.