论文标题
分层相关聚类和树木保存嵌入
Hierarchical Correlation Clustering and Tree Preserving Embedding
论文作者
论文摘要
我们提出了一种层次相关聚类方法，该方法扩展了众所周知的相关聚类，以产生适用于正成对差异的分层群集。然后，在以下内容中，我们研究了无监督的表示学习，并使用这种层次相关性聚类进行研究。为此，我们首先调查嵌入相应的层次结构，用于保存树木的嵌入和特征提取。此后，我们研究了Minimax距离测量与相关聚类的扩展，作为另一个表示范式。最后，我们在几个数据集上演示了方法的性能。
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.
