Hierarchical Correlation Clustering and Tree Preserving Embedding
This work addresses clustering and feature extraction challenges in unsupervised learning, but appears incremental as it builds on existing correlation clustering and minimax distance measures.
The authors tackled the problem of hierarchical clustering and representation learning for data with both positive and negative dissimilarities by extending correlation clustering to produce hierarchical clusters and developing tree preserving embedding methods, demonstrating performance on several datasets.
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.