This paper presents a novel dimensionality reduction technique: ELM-SOM. This technique preserves the intrinsic quality of Self-Organizing Maps (SOM): It is nonlinear and suitable for big data. It also brings continuity to the projection using two Extreme Learning Machine (ELM) models, the first one to perform the dimensionality reduction and the second one to perform the reconstruction. ELM-SOM is tested successfully on six diverse datasets. Regarding reconstruction error, ELM-SOM is comparable to SOM while bringing continuity.
|Title of host publication||2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 10.10.2018|
|MoE publication type||A4 Article in conference proceedings|
|Event||2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil|
Duration: 08.07.2018 → 13.07.2018
- 512 Business and Management