Extreme Learning Machines for Visualization+r: Mastering Visualization with Target Variables

Abstract

The Current Paper Presents an Improvement of the Extreme Learning Machines for Visualization (Elmvis+) Nonlinear Dimensionality Reduction Method. in This Improved Method, Called Elmvis+r, It is Proposed to Apply the Originally Unsupervised Elmvis+ Method for the Regression Problems, using Target Values to Improve Visualization Results. It Has Been Shown in Previous Work that the Approach of Adding Supervised Component for Classification Problems Indeed Allows to Obtain Better Visualization Results. to Verify This Assumption for Regression Problems, a Set of Experiments on Several Different Datasets Was Performed. the Newly Proposed Method Was Compared to the Elmvis+ Method And, in Most Cases, Outperformed the Original Algorithm. Results, Presented in This Article, Prove the General Idea that using Supervised Components (Target Values) with Nonlinear Dimensionality Reduction Method Like Elmvis+ Can Improve Both Visual Properties and overall Accuracy.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Artificial neural networks; Cosine similarity; Extreme learning machines; Machine learning; Nonlinear dimensionality reduction; Nonlinear regression; Visualization

International Standard Serial Number (ISSN)

1866-9964; 1866-9956

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Jun 2018

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