Paris A. Karakasis
A Short Introduction to Canonical Correlation Analysis
Oftentimes, we have the ability to observe and process different data sources/modalities, like text, sound, image, and video, jointly. The motivation behind handling them jointly is multifold as data fusion enables understanding associations and dependencies across different data sources, but also improving our estimation capabilities under adverse conditions (e.g. presence of strong noise or inferences). In many applications, the task of interest is the one of detecting and estimating common latent factors indirectly based on the observed modalities (a.k.a. views). As we will discuss, this task can be tackled after considering Canonical Correlation Analysis (CCA), a powerful multivariate statistical method that aims to uncover latent relationships between two (or more) data sources. In this tutorial, we will explore the fundamental concepts and principles of CCA, its underlying assumptions, and the step-by-step process of performing CCA. We will also discuss the interpretation of CCA as generative model, both in the linear and nonlinear regimes, as well as its practical applications in multimodal learning, graph mining, communications, and biomedical signal processing.
About the Speaker
Paris A. Karakasis (Graduate Student Member, IEEE) received the Diploma and M.Sc. degrees in electrical and computer engineering from the Technical University of Crete, Chania, Greece, in 2017 and 2019, respectively. He is currently pursuing the Ph.D. degree with the Electrical and Computer Engineering Department, University of Virginia, Charlottesville, VA, USA. His research interests include signal processing, numerical optimization, machine learning, tensor decomposition, and graph mining.