  We propose a hierarchical correlation clustering method that extends the
well-known correlation clustering to produce hierarchical clusters applicable
to both positive and negative pairwise dissimilarities. Then, in the following,
we study unsupervised representation learning with such hierarchical
correlation clustering. For this purpose, we first investigate embedding the
respective hierarchy to be used for tree preserving embedding and feature
extraction. Thereafter, we study the extension of minimax distance measures to
correlation clustering, as another representation learning paradigm. Finally,
we demonstrate the performance of our methods on several datasets.
