A graph-structured distance for heterogeneous datasets with meta variables

Heterogeneous datasets emerge in various machine learning or optimization applications that feature different data sources, various data types and complex relationships between variables. In practice, heterogeneous datasets are often partitioned into smaller well-behaved ones that are easier to process. However, some applications involve expensive-to-generate or limited size datasets, which motivates methods based on the whole … Read more