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Seminar Abstracts
Fernando de la Torre, Computer Science, Carnegie Mellon University"Learning the representation for modeling, classification and clustering problems with energy-based component analysis methods" 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 of geometric
transformations (e.g. translation), effects of noise and missing data or how to
select 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/tracking (e.g. active appearance models) problems. In this talk I
will emphasize how many learning algorithms are related to an optimization
problem, and I will show different optimization strategies to learn from very
high dimensional data.
TUESDAY, April 3, 2007 |