Speaker |
Stefanos Zafeiriou |
|---|---|
Affiliation |
Imperial College London |
Date |
Friday, 27 April 2018 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts Building 309 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
Discovering correlations in signals is a very important problem in the intersection of statistics and machine learning. Arguably the most used tool to this end in Canonical Correlation Analysis (CCA). CCA has certain limitations when it is used to model correlations in real world signals. First it discovers only the most correlated spaces, ignoring the individual spaces between signals. Second it is a linear method that is optimal under Gaussian noise, hence (a) it fails when gross outliers are present in the signals and (b) it cannot model non-linear correlations. In this talk, I will present recent advancements in CCA, as well as methods for discovering both the individual, as well as the most correlated components that are robust to gross outliers, as well as can model non-linear correlations. I will demonstrate applications in computer vision and signal processing |
Biography |