Abstract
This Paper Presents an Improvement of the ELMVIS+ Method that is Proposed for Fast Nonlinear Dimensionality Reduction. the ELMVIS++C Has an Additional Supervised Learning Component Compared to ELMVIS+, Which is Originally an Unsupervised Method as Like the Majority of the Other Dimensionality Reduction Method. This Component Prevents Samples under the Same Class Being Separated Apart from Each Other. in This Improved Method, the Importance of the Supervised Component Can Be Further Tuned to Have Different Level of Influence. the Test Results on Four Datasets Indicate that the Proposed Improvement Not Only Maintains the Performance of ELMVIS+, But Also is Extremely Beneficial for Certain Applications Where the Visualization of the Data in Relation with the Class Becomes an Important Issue.
Recommended Citation
A. Gritsenko et al., "Combined Nonlinear Visualization and Classification: ELMVIS++C," Proceedings of the International Joint Conference on Neural Networks, pp. 2617 - 2624, article no. 7727527, Institute of Electrical and Electronics Engineers, Oct 2016.
The definitive version is available at https://doi.org/10.1109/IJCNN.2016.7727527
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-150900619-9
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
31 Oct 2016
