[IPOL announce] new article: CAEclust: A Consensus of Autoencoders Representations for Clustering

[announcements about the IPOL journal]

A new article is available in IPOL: http://www.ipol.im/pub/art/2022/398/

Séverine Affeldt, Lazhar Labiod, and Mohamed Nadif,
CAEclust: A Consensus of Autoencoders Representations for Clustering,
Image Processing On Line, 12 (2022), pp. 590–603.
https://doi.org/10.5201/ipol.2022.398


Abstract
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.