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Foundations of Robotics
Seminar, March 28, 2007
Time
and Place | Seminar Abstract | Speaker
Appointments
Learning the
representation for modeling, classification and clustering problems with
energy-based component analysis methods
Fernando De la Torre
NSH 1507
Refreshments 4:15 pm
Talk 4:30 pm
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.
For appointments, please contact Fernando De
la Torre.
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.