Snapshot Spectral Clustering -- a costless approach to deep clustering
  ensembles generation
Adam Piróg Halina Kwaśnicka
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.