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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.
In the first part of the talk, I will review standard CA techniques (Principal Component Analysis, Canonical Correlation Analysis, Linear Discrimiant Analysis, Non-negative Matrix Factorization, Independent Component Analysis) and three standard extensions (Kernel methods, latent variable models and tensors). In the second part of the talk, I will describe a unified framework for energy-based learning in CA methods. I will also 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.
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Pradeep Ravikumar Last modified: Thu Mar 29 18:45:00 EDT 2007