Sparse Modeling for High-Dimensional Multi-Manifold Data Analysis
Mar 17, 2014
from 10:00 AM to 12:00 PM
|Where||Engr. IV Bldg., Shannon Room 54-134|
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University of California, Berkeley
One of the most fundamental challenges facing scientists and engineers across different fields, such as signal/image processing, computer vision, robotics and bioinformatics, is the large amounts of high-dimensional data that need to be analyzed and understood. In this talk, I present efficient and theoretically guaranteed algorithms, based on the sparse representation theory, for the analysis of high-dimensional datasets by exploiting their underlying low-dimensional structures. I talk about algorithms for the two fundamental problems of clustering and subset selection in unions of subspaces and discuss the robustness of the algorithms to data nuisances. I show that these tools effectively advance the state-of-the-art data analysis in a wide range of important real-world problems, such as segmentation of motions in videos, clustering of images of objects, active learning and identification of hybrid dynamical systems.
Ehsan Elhamifar is a postdoctoral scholar in the department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He obtained his PhD in Electrical and Computer Engineering from the Johns Hopkins University. Ehsan is broadly interested in developing provably correct and efficient data analysis algorithms that can address the challenges of complex and large-scale high-dimensional datasets. Specifically, he focuses on the intrinsic low-dimensionality of the data and uses tools from convex analysis, sparse representation and compressive sensing to develop such algorithms. Ehsan obtained MSE and MS degrees in Applied Mathematics and Statistics and Electrical Engineering, respectively, from the Johns Hopkins University and Sharif University of Technology in Iran. Before that, he earned his BS with Honors in Biomedical Engineering from Amirkabir University of Technology, Iran.