Section: New Results

Transfer learning in model-based clustering

Participants : Christophe Biernacki, Alexandre Lourme.

In many situations one needs to cluster several datasets, possibly arising from different populations, instead of a single one, into partitions with identical meaning and described by similar features. Such situations involve commonly two kinds of standard clustering processes. The samples are clustered traditionally either as if all units arose from the same distribution, or on the contrary as if the samples came from distinct and unrelated populations. But a third situation should be considered: As the datasets share statistical units of same nature and as they are described by features of same meaning, there may exist some link between the samples.

We propose a linear stochastic link between the samples, what can be justified from some simple but realistic assumptions, both in the Gaussian and in the $t$ mixture model-based clustering context ([15] and a paper in revision). In the general context (categorical or heterogeneous variables), we propose to use alternatively an entropic link between populations [17] . All these works are related to the Lourme's PhD thesis [11] .

A chapter of book about transfer learning (including clustering, classification and regression) is currently submitted for publication (joint work with Julien Jacques and Alexandre Lourme).