  This paper studies the problem of learning clusters which are consistently
present in different (continuously valued) representations of observed data.
Our setup differs slightly from the standard approach of (co-) clustering as we
use the fact that some form of `labeling' becomes available in this setup: a
cluster is only interesting if it has a counterpart in the alternative
representation. The contribution of this paper is twofold: (i) the problem
setting is explored and an analysis in terms of the PAC-Bayesian theorem is
presented, (ii) a practical kernel-based algorithm is derived exploiting the
inherent relation to Canonical Correlation Analysis (CCA), as well as its
extension to multiple views. A content based information retrieval (CBIR) case
study is presented on the multi-lingual aligned Europal document dataset which
supports the above findings.
