Separating Boundary Points via Structural Regularization for Very Compact Clusters

TL;DR

This paper introduces VCC, a deep clustering method that leverages local relationships near cluster boundaries to produce very compact and well-separated clusters across diverse data types.

Contribution

The paper proposes VCC, an end-to-end deep clustering algorithm that focuses on boundary point separation using structural regularization, applicable to both image and non-image data.

Findings

Achieves competitive performance on various datasets.

Effectively separates boundary points for compact clusters.

Works well on both image and non-image data.

Abstract

Clustering algorithms have significantly improved along with Deep Neural Networks which provide effective representation of data. Existing methods are built upon deep autoencoder and self-training process that leverages the distribution of cluster assignments of samples. However, as the fundamental objective of the autoencoder is focused on efficient data reconstruction, the learnt space may be sub-optimal for clustering. Moreover, it requires highly effective codes (i.e., representation) of data, otherwise the initial cluster centers often cause stability issues during self-training. Many state-of-the-art clustering algorithms use convolution operation to extract efficient codes but their applications are limited to image data. In this regard, we propose an end-to-end deep clustering algorithm, i.e., Very Compact Clusters (VCC). VCC takes advantage of distributions of local relationships of samples near the boundary of clusters, so that they can be properly separated and pulled to cluster centers to form compact clusters. Experimental results on various datasets illustrate that our proposed approach achieves competitive clustering performance against most of the state-of-the-art clustering methods for both image and non-image data, and its results can be easily qualitatively seen in the learnt low-dimensional space.