Unsupervised cross-domain learning by interaction information co-clustering

In real-world data mining applications, one often has access to multiple datasets that are relevant to the task at hand. However, learning from such datasets can be difficult as they are often drawn from different domains, i.e., not identically distributed or differ in class or feature sets. In this paper, we consider the problem of learning the class structures of related domains in an unsupervised manner. Its setting generalizes that of information filtering and novelty detection applications which addresses both known and unknown classes. We propose a co-clustering framework for estimating and adapting the class structures of two related domains, enabling the analyses of shared and unique classes. We define an objective function using interaction information to take account of the divergence between the corresponding clusters of respective domains. We present an iterative algorithm which alternates object and feature clustering and converges to a local minimum of the objective function. We present empirical results using text benchmarks, comparing the proposed algorithm and combinations of conventional approaches in problems of partitioning documents and detecting unknown topics.