Elm-Som+: A Continuous Mapping for Visualization

Author

Renjie Hu
Karl Ratner
Edward Ratner
Yoan Miche
Kaj Mikael Björk
Amaury Lendasse, Missouri University of Science and TechnologyFollow

Abstract

This Paper Presents a Novel Dimensionality Reduction Technique based on Elm and Som: Elm-Som+. This Technique Preserves the Intrinsic Quality of Self-Organizing Map (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 Nine Diverse Datasets. Regarding Reconstruction Error, the New Methodology Shows Considerable Improvement over Som and Brings Continuity.

Recommended Citation

R. Hu et al., "Elm-Som+: A Continuous Mapping for Visualization," Neurocomputing, vol. 365, pp. 147 - 156, Elsevier, Nov 2019.

The definitive version is available at https://doi.org/10.1016/j.neucom.2019.06.093

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 Serial Number (ISSN)

1872-8286; 0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

06 Nov 2019