CAEclust: A Consensus of Autoencoders Representations for Clustering
Séverine Affeldt, Lazhar Labiod, Mohamed Nadif
Séverine Affeldt, Lazhar Labiod, and Mohamed Nadif, CAEclust: A Consensus of Autoencoders Representations for Clustering, Image Processing On Line, 12 (2022), pp. 590–603.

Communicated by José Lezama
Demo edited by Jérémy Anger


The CAEclust Python package implements an original deep spectral clustering in an ensemble framework. Recently, strategies combining classical clustering approaches and deep autoencoders have been proposed, but their effectiveness is impeded by deep network hyperparameters settings. We alleviate this issue with a consensus solution that hinges on the fusion of multiple deep autoencoder representations and spectral clustering. CAEclust offers an efficient merging of encodings by using the landmarks strategy and demonstrates its effectiveness on benchmark data. CAEclust enables to reproduce our experiments and explore novel datasets.