Hierarchical Correlation Clustering and Tree Preserving Embedding
Abstract
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.
Cite
Text
Chehreghani and Chehreghani. "Hierarchical Correlation Clustering and Tree Preserving Embedding." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.02178Markdown
[Chehreghani and Chehreghani. "Hierarchical Correlation Clustering and Tree Preserving Embedding." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/chehreghani2024cvpr-hierarchical/) doi:10.1109/CVPR52733.2024.02178BibTeX
@inproceedings{chehreghani2024cvpr-hierarchical,
title = {{Hierarchical Correlation Clustering and Tree Preserving Embedding}},
author = {Chehreghani, Morteza Haghir and Chehreghani, Mostafa Haghir},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {23083-23093},
doi = {10.1109/CVPR52733.2024.02178},
url = {https://mlanthology.org/cvpr/2024/chehreghani2024cvpr-hierarchical/}
}