Numerical Analysis and Scientific Computing Seminar
Scalable Optimization Algorithms for Large-scale Subspace Clustering
Speaker: Daniel Robinson, Johns Hopkins University
Location: Warren Weaver Hall 1302
Date: March 29, 2019, 10 a.m.
I present recent work on the design of scalable optimization algorithms for aiding in the big data task of subspace clustering. In particular, I will describe three approaches that we recently developed to solve optimization problems constructed from the so-called self-expressiveness property of data that lies in the union of low-dimensional subspaces. Sources of data that lie in the union of low-dimensional subspaces include multi-class clustering and motion segmentation. Our optimization algorithms achieve scalability by leveraging three features: a rapidly adapting active-set approach, a greedy optimization method, and a divide-and-conquer technique. Numerical results demonstrating the scalability of our approaches will be presented.
Daniel P. Robinson received his Ph.D. from the University of California at San Diego in 2007. He spent the next three years working with Nicholas I. M. Gould and Jorge Nocedal as a Postdoctoral Researcher in the Mathematical Institute at the University of Oxford and the Department of Industrial Engineering and Management Sciences at Northwestern University. In 2011 he joined the Department of Applied Mathematics and Statistics in the Whiting School of Engineering at Johns Hopkins University. His primary research area is optimization with specific interest in the design, analysis, and implementation of efficient algorithms for large-scale convex and nonconvex problems, with particular interest in applications related to computer vision and medicine/healthcare. He is a member of the Society of Industrial and Applied Mathematics (SIAM), Mathematical Optimization Society (MOS), the American Mathematical Society (AMS), and the Institute for Operations Research and the Management Sciences (INFORMS), in addition to being the INFORMS Vice-Chair for Nonlinear Optimization (2014-2016). Daniel has also served as the cluster chair for the 2016 International Conference on Continuous Optimization (ICCOPT) in Tokyo, Japan, a Program Committee member for the 2017 AAAI Conference on Artificial Intelligence in San Francisco, California, and is currently an Associated Editor for the journal Computational Optimization and Applications. Finally, Daniel has been awarded the Professor Joel Dean Award for Excellence in Teaching at Johns Hopkins University in 2012 and 2018.