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.

翻译：我们提出了一种层次相关聚类方法，该方法将经典的相关聚类扩展到能产生适用于正负成对差异性的层次聚类。随后，我们研究了基于此类层次相关聚类的无监督表示学习。为此，我们首先探讨了将相应层次结构嵌入用于树保持嵌入和特征提取的方法。接着，我们研究了极小化极大距离度量在相关聚类中的扩展，作为另一种表示学习范式。最后，我们在多个数据集上验证了所提方法的性能。