Localized Multiple Sources Self-Organizing Map

Ludovic Platon 1 Farida Zehraoui 1 Fariza Tahi 1
1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
Abstract : We present in this paper a new approach based on unsupervised self organizing maps called MSSOM. This approach combines multiple heterogeneous data sources and learns the weights of each source at the level of clusters instead of learning the same source weights for the whole space. This allows to improve the performances of our model especially in applications where a local feature selection is important. We evaluate our method using several artificial and real datasets and show competitive results compared to the state-of-art.
Keywords : Dimensionality reduction Kernel methods Neural network Self-Organizing Map Unsupervised learning
Document type :
Conference papers
Complete list of metadatas
https://hal.archives-ouvertes.fr/hal-01971022
Contributor : Frédéric Davesne <>
Submitted on : Sunday, January 6, 2019 - 5:51:58 PM
Last modification on : Monday, October 28, 2019 - 10:50:22 AM

Identifiers

Collections

IBISC | UNIV-EVRY | IBISC-AROBAS | UNIV-PARIS-SACLAY

Citation

Ludovic Platon, Farida Zehraoui, Fariza Tahi. Localized Multiple Sources Self-Organizing Map. 25th International Conference on Neural Information Processing (ICONIP 2018), Dec 2018, Siem Reap, Cambodia. pp.648--659, ⟨10.1007/978-3-030-04182-3_57⟩. ⟨hal-01971022⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

195

Contact

Informations

CCSD