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Hierarchical Correlation Clustering and Tree Preserving Embedding

Morteza Haghir Chehreghani · Mostafa Haghir Chehreghani

Arch 4A-E Poster #345
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Fri 21 Jun 10:30 a.m. PDT — noon PDT


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.

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