Snapshot Spectral Clustering -- a costless approach to deep clustering ensembles generation

TL;DR

This paper introduces Snapshot Spectral Clustering, a cost-effective deep clustering ensemble method that leverages multiple data views to improve clustering performance without high computational costs.

Contribution

It proposes a novel deep clustering ensemble approach that efficiently combines multiple data views, bridging deep neural networks, clustering, and ensemble learning.

Findings

Proves the effectiveness of the proposed method through experiments

Demonstrates improved clustering results over baseline methods

Provides guidance on hyperparameter selection for the approach

Abstract

Despite tremendous advancements in Artificial Intelligence, learning from large sets of data in an unsupervised manner remains a significant challenge. Classical clustering algorithms often fail to discover complex dependencies in large datasets, especially considering sparse, high-dimensional spaces. However, deep learning techniques proved to be successful when dealing with large quantities of data, efficiently reducing their dimensionality without losing track of underlying information. Several interesting advancements have already been made to combine deep learning and clustering. Still, the idea of enhancing the clustering results by combining multiple views of the data generated by deep neural networks appears to be insufficiently explored yet. This paper aims to investigate this direction and bridge the gap between deep neural networks, clustering techniques and ensemble learning methods. To achieve this goal, we propose a novel deep clustering ensemble method - Snapshot Spectral Clustering, designed to maximize the gain from combining multiple data views while minimizing the computational costs of creating the ensemble. Comparative analysis and experiments described in this paper prove the proposed concept, while the conducted hyperparameter study provides a valuable intuition to follow when selecting proper values.