Why Label When You Can Compare? Active Constraint Pursuit in Metric Learning and Clustering
Associate Professor of Electrical Engineering and Computer Science
University of Michigan
Tuesday, October 07, 2014|
4:00pm - 5:30pm
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About the Event
Relating pairs of samples, by way of similarity functions or distance metrics, is at the heart of machine learning. In some applications, such as face-photo organization in which we do not know the identities in the photos, specifying pairwise links is the only way to handle the learning problem—and even naive users can provide the annotations. In this talk, I will cover my recent work in metric learning and active pairwise constraint pursuit. For metric learning, I will present our efficient max-margin metric learning that learns a Mahalanobis metric, as well as our random forest distance method that specifies a space-varying non-linear distance function. In active constraint pursuit for semi-supervised clustering, I will discuss how we use the gradient of the spectral decomposition to select the next best constraints for active user queries. I will present applications of these methods to real data.
Corso is an associate professor of Electrical Engineering and Computer Science at the University of Michigan. He received his PhD and MSE degrees at The Johns Hopkins University in 2005 and 2002, respectively, and the BS Degree with honors from Loyola College In Maryland in 2000, all in Computer Science. He spent two years as a post-doctoral fellow at the University of California, Los Angeles. From 2007-14 he was a member of the Computer Science and Engineering faculty at SUNY Buffalo. He is the recipient of the Army Research Office Young Investigator Award 2010, NSF CAREER award 2009, SUNY Buffalo Young Investigator Award 2011, a member of the 2009 DARPA Computer Science Study Group, and a recipient of the Link Foundation Fellowship in Advanced Simulation and Training 2003. Corso has authored more than ninety peer-reviewed papers on topics of his research interest including computer vision, robot perception, data science, and medical imaging. He is a member of the AAAI, IEEE and the ACM.
Open to: Public