Abstract
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.
Recommended Citation
R. Hu et al., "ELM-SOM: A Continuous Self-Organizing Map for Visualization," Proceedings of the International Joint Conference on Neural Networks, article no. 8489268, Institute of Electrical and Electronics Engineers, Oct 2018.
The definitive version is available at https://doi.org/10.1109/IJCNN.2018.8489268
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Dimensionality Reduction Techniques; Extreme Learning Machines; Machine Learning; Neural Networks; Self-Organizing Maps; Visualization
International Standard Book Number (ISBN)
978-150906014-6
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
10 Oct 2018
