CHESS

Talk (~40mn) :

Dana Lahat++ : When blind source separation meets data fusion

Abstract: Blind source separation (BSS) is a well-established framework for data analysis. Independent component analysis (ICA) and algorithms such as JADE, SOBI, FastICA and InfoMax, designed in the 1990's, are now regarded as standard analytical tools in a broad range of domains, and are almost synonymous with BSS. However, for various types of real-life data, their underlying assumptions are too restrictive or insufficient. In fact, from the very early days of BSS, other types of models have been proposed that can better accommodate the richer properties of the data. In this talk, we focus on two of these directions: relaxing the assumption of independence within a single data set, and allowing statistical dependence between different data sets. The latter opens new horizons and opportunities in data fusion and multiset data analysis. We present a few recently-proposed models, algorithms, and new results on uniqueness.