Zeeshan Khawar Malik
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.
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 |
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