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Wed Nov 7, 3 pm. Talk on Sparse Learning

Author:movellan @ November 1st, 2007 Leave a Comment

TALK, Wednesday, November 7, 3 – 4:30 PM, EBU3-4140

Subspectral Algorithms for Sparse Learning, Optimization & Inference

Baback Moghaddam
Jet Propulsion Laboratory
California Institute of Technology

I will present a general class of “subspectral” algorithms (sparse
eigenvector techniques) for solving NP-hard combinatorial optimization
problems in three applied domains: (Un)Supervised Learning (e.g. PCA &
LDA), Quadratic/Entropic Optimization (e.g. Least-Squares & MaxEnt)
and 3) Bayesian Inference (e.g. Automatic Relevance Determination).
Efficient algorithms for both optimal and approximate greedy solutions
are derived using analytic eigenvalue bounds. Sample applications
presented include “sparse PCA” for variable selection (in statistics),
“sparse LDA” for classification (gene discovery), sparse kernel
regression (robotics & control), sparse quadratic programming
(portfolio optimization), graph model selection (sensor networks) and
sparse Bayesian inference for computer vision (face recognition &
OCR).

*Biography:* Baback Moghaddam joined the Jet Propulsion Laboratory in 2007
as a Principal Member, with the Machine Learning and Instrument Autonomy Group.
Prior to JPL he was a Senior Research Scientist at the Mitsubishi Electric Research
Laboratory (MERL) since 1997. He received his Ph.D. in Electrical Engineering and
Computer Science from the Massachusetts Institute of Technology in 1997, where
he was a member of the Vision & Modeling Group of the MIT Media Laboratory
since 1992. As part of his doctoral work at MIT he developed an automatic face
recognition system which won the 1996 DARPA “FERET” Face Recognition
competition. He has authored numerous articles on 2D face recognition and 3D
facial modeling in leading journals and conferences, including the core chapter in
Springer-Verlag’s Biometric Series, The Handbook of Face Recognition.