Prof. Cédric Heuchenne Senior lecturer at HEC - University of Liège, Belgium. Scientific Advisor at International Research Institute for Artificial Intelligence and Data Science (IAD), Dong A University. Published more than 40 articles in prestigious peer-reviewed scientific journals (with impact factor and indexed by Scopus), more than 30 articles presented at international conferences, and he has been invited by more than 30 universities for seminars, workshops, and research stay.

Title: Statistical Matching using KCCA, Super-OM and Autoencoders-CCA

Abstract: 

The potential to study and improve different aspects of our lives is ever growing thanks to the abundance of data available in today’s modern society. Scientists and researchers often need to analyze data from different sources; the observations, which only share a subset of the variables, can not always be paired to detect common individuals. This is the case, for example, when the information required to study a certain phenomenon is coming from different sample surveys. Statistical matching is a common practice to combine these data sets. In this talk, we investigate and extend to statistical matching three methods based on Kernel Canonical Correlation Analysis (KCCA), Super-Organizing Map (Super-OM) and Autoencoders-Canonical Correlation Analysis (A-CCA). These methods are designed to deal with various variable types, sample weights and incompatibilities among categorical variables. We use the 2017 Belgian Statistics on Income and Living Conditions (SILC) and we compare the performance of the proposed statistical matching methods by means of a cross-validation technique, as if the data were available from two separate sources.: