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Subspace methods (e.g. Principal Component Analysis, Independent Component
Analysis, Linear Discriminant Analysis, ...) have been successfully applied
in numerous visual, graphics and signal processing tasks over the last
two decades. In this talk, I will provide a unified framework for several novel
component-analysis techniques useful for modeling, classifying and
clustering huge amounts of high dimensional data. In particular, I will describe
five novel component-analysis techniques:
- Robust parameterized component Analysis (RPCA): Extension of
principal component analysis (PCA) to build a linear model robust to
outliers and invariant to geometric transformations.
- Multimodal Oriented Component Analysis (MODA): Generalization of
linear discriminant analysis (LDA) optimal for Gaussian multimodal classes
with different covariances.
- Representational Oriented Component Analysis (ROCA): Extension of OCA
to improve classification accuracy when few training samples are available
(e.g. just 1 training sample).
- Discriminative Cluster Analysis (DCA): Unsupervised low dimensional
reduction method that finds a subspace well suited for k-means clustering.
- Dynamic Couple Component Analysis (DCCA): Generalization of
PCA method to learn relations between multiple high dimensional data sets in
presence of limited training data.
I will discuss how these techniques can be applied to visual tracking,
signal modeling (e.g. background estimation, virtual avatars) and
pattern recognition problems (e.g. face recognition), as well as
clustering long term multimodal data (video, audio, body sensors) useful
to monitor our daily activity.
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