An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data

Malik, Zeeshan Khawar; Hussain, Amir; Wu, Jonathan

Abstract

This paper presents a generalized incremental Laplacian Eigenmaps (GENILE), a novel online version of the Laplacian Eigenmaps, one of the most popular manifold-based dimensionality reduction techniques which solves the generalized eigenvalue problem. We evaluate the comparative performance of the manifold-based learning techniques using both artificial and real data. Specifically, two popular artificial datasets: swiss roll and s-curve datasets, are used, in addition to real MNIST digits, bank-note and heart disease datasets for testing and evaluating our novel method benchmarked against a number of standard batch-based and other manifold-based learning techniques. Preliminary experimental results demonstrate consistent improvements in the classification accuracy of the proposed method in comparison with other techniques.

Citation

Malik, Z. K., Hussain, A., & Wu, J. (2016). An online generalized eigenvalue version of Laplacian Eigenmaps for visual big data. Neurocomputing, 173(2), 127-136. https://doi.org/10.1016/j.neucom.2014.12.119

• Journal Article Type: Article
• Acceptance Date: Dec 12, 2014
• Online Publication Date: Sep 3, 2015
• Publication Date: Jan 15, 2016
• Deposit Date: Oct 7, 2019
• Journal: Neurocomputing
• Print ISSN: 0925-2312
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 173
• Issue: 2
• Pages: 127-136
• DOI: https://doi.org/10.1016/j.neucom.2014.12.119
• Keywords: Dimensionality reduction; Generalized eigenvalue problem; Laplacian Eigenmaps; Manifold-based learning
• Public URL: http://researchrepository.napier.ac.uk/Output/1792651