Closed-Loop Data Transcription via Rate Reduction

In this talk we introduce a principled computational framework for learning a compact structured representation for real-world datasets. More specifically, we propose to learn a closed-loop transcription between the distribution of a high-dimensional multi-class dataset and an arrangement of multiple independent subspaces, known as a linear discriminative representation (LDR). We argue that the encoding and decoding mappings of the transcription naturally form a closed-loop sensing and control system. The optimality of the closed-loop transcription, in terms of parsimony and self-consistency, can be characterized in closed-form by an information-theoretic measure known as the rate reduction. The optimal encoder and decoder can be naturally sought through a two-player minimax game over this principled measure. To a large extent, this new framework unifies concepts and benefits of auto-encoding and GAN and generalizes them to the settings of learning a both discriminative and generative representation for multi-class visual data. This work opens many new mathematical problems regarding learning linearized representations for nonlinear submanifolds in high-dimensional spaces, as well as suggests potential computational mechanisms about how visual memory of multiple object classes could be formed jointly or incrementally through a purely internal closed-loop feedback process.

Brief Bio of the Speaker: Yi Ma is a Professor at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. His research interests include computer vision, high-dimensional data analysis, and intelligent systems. Yi received his Bachelor’s degrees in Automation and Applied Mathematics from Tsinghua University in 1995, two Masters degrees in EECS and Mathematics in 1997, and a PhD degree in EECS from UC Berkeley in 2000.  He has been on the faculty of UIUC ECE from 2000 to 2011, the principal researcher and manager of the Visual Computing group of Microsoft Research Asia from 2009 to 2014, and the Executive Dean of the School of Information Science and Technology of ShanghaiTech University from 2014 to 2017. He then joined the faculty of UC Berkeley EECS in 2018. He has published about 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized principal component analysis, and high-dimensional data analysis. He received the NSF Career award in 2004 and the ONR Young Investigator award in 2005. He also received the David Marr prize in computer vision from ICCV 1999 and best paper awards from ECCV 2004 and ACCV 2009. He has served as the Program Chair for ICCV 2013 and the General Chair for ICCV 2015. He is a Fellow of IEEE, ACM, and SIAM.