Hierarchical Correlation Clustering and Tree Preserving Embedding

2020-02-18

Computer Vision and Pattern Recognition

AI Summary

Traditional correlation clustering struggles to model hierarchical structures and handle heterogeneous pairwise similarities—both positive and negative—within a unified framework. Method: We propose Hierarchical Correlation Clustering (HCC), the first correlation clustering formulation explicitly designed for hierarchical settings. HCC jointly optimizes cluster hierarchy and low-dimensional representations via an unsupervised tree-preserving embedding scheme. Furthermore, we generalize the minimax distance metric to correlation clustering, yielding a robust, noise-resilient dissimilarity measure tailored to heterogeneous pairwise constraints. Contribution/Results: Extensive experiments on standard benchmarks demonstrate that HCC significantly improves hierarchical clustering quality, embedding fidelity, and downstream task performance over state-of-the-art methods. By unifying hierarchy learning, representation learning, and robust constraint modeling in an unsupervised setting, HCC establishes a novel paradigm for unsupervised hierarchical representation learning.

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 hier-archical 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.

Problem

• Clustering
• Hierarchical Feature Learning
• Similarity Measurement Optimization

Innovation

• Hierarchical Clustering
• Unsupervised Feature Learning
• MinMax Distance Measurement