RI | Seminar | April 6, 2007

Robotics Institute Seminar, April 6, 2007
Time and Place | Seminar Abstract | Speaker Biography | Speaker Appointments

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Learning the representation for modeling, classification and clustering problems with energy-based component analysis methods

Fernando De la Torre

Research Scientist

Robotics Institute
Carnegie Mellon University

Time and Place

Mauldin Auditorium (NSH 1305)
Refreshments 3:15 pm
Talk 3:30 pm

RoboCast

Abstract

Selecting a good representation of the data is a key aspect of the success of any modeling, classification or clustering algorithm. Component Analysis (CA) methods (e.g. Kernel Principal Component Analysis, Independent Component Analysis, Tensor factorization) have been used as a feature extraction step for modeling, classification and clustering in numerous visual, graphics and signal processing tasks over the last four decades. CA techniques are especially appealing because many can be formulated as eigen-problems, offering great potential for efficient learning of linear and non-linear representations of the data without local minima. However, the eigen-formulation often hides important aspects of making the learning successful such as understanding normalization factors, how to build invariant representations (e.g. to geometric transformation), effects of noise and missing data or how to learn the kernel. In this talk, I will describe a unified framework for energy-based learning in CA methods. I will point out how apparently different learning tasks (clustering, classification, modeling) collapse into a single task when viewed from the perspective of energy functions. Moreover, I will propose several extensions of CA methods to learn linear and non-linear representations of data to improve performance, over the current use of CA features, in state-of-the-art algorithms for classification (e.g. support vector machines), clustering (e.g. spectral graph methods) and modeling/visual tracking (e.g. active appearance models) problems.

Speaker Biography

Fernando De la Torre received his B.Sc. degree in Telecommunications, M.Sc. and Ph.D degrees in Electronic Engineering, respectively, in 1994, 1996 and 2002, from La Salle School of Engineering in Ramon Llull University, Barcelona, Spain. In 1997 and 2000 he was an Assistant and Associate Professor in the Department of Communications and Signal Theory in Enginyeria La Salle. Since 2005 he has been a Research Scientist in the Robotics Institute at Carnegie Mellon University. Dr. De la Torre's research interests include computer vision and machine learning, in particular face analysis, optimization and component analysis methods. Dr. De la Torre has co-organized the first workshop on component analysis methods for modeling, classification and clustering problems in computer vision in conjunction with CVPR'07 and the workshop on human sensing from video jointly with CVPR'06. He has also given several tutorials at international conferences (ECCV'06, CVPR'06, ICME'07) on the use and extensions of component analysis methods. Fernando has served on the programme committee for a number of top international computer vision and machine learning conferences including CVPR, ICCV, ECCV, NIPS, ICML, and ICIP.

Speaker Appointments

For appointments, please contact Janice Brochetti (janiceb@cs.cmu.edu)

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The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.