Deep Architectures for Joint Clustering and Visualization with Self-organizing Maps - Archive ouverte HAL
Proceedings/Recueil Des Communications Année : 2019

Deep Architectures for Joint Clustering and Visualization with Self-organizing Maps

Résumé

Recent research has demonstrated how deep neural networks are able to learn representations to improve data clustering. By considering representation learning and clustering as a joint task, models learn clustering-friendly spaces and achieve superior performance, compared with standard two-stage approaches where dimensionality reduction and clustering are performed separately. We extend this idea to topology-preserving clustering models, known as self-organizing maps (SOM). First, we present the Deep Embedded Self-Organizing Map (DESOM), a model composed of a fully-connected autoencoder and a custom SOM layer, where the SOM code vectors are learnt jointly with the autoencoder weights. Then, we show that this generic architecture can be extended to image and sequence data by using convolutional and recurrent architectures, and present variants of these models. First results demonstrate advantages of the DESOM architecture in terms of clustering performance, visualization and training time.
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Dates et versions

hal-04510497 , version 1 (19-03-2024)

Identifiants

Citer

Florent Forest, Mustapha Lebbah, Hanane Azzag, Jérôme Lacaille. Deep Architectures for Joint Clustering and Visualization with Self-organizing Maps. Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2019, 11607, Springer International Publishing, pp.105-116, 2019, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-26142-9_10⟩. ⟨hal-04510497⟩
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