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

Citation

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

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.

Keywords
Dimensionality reduction; Generalized eigenvalue problem; Laplacian Eigenmaps; Manifold-based learning

Journal
Neurocomputing: Volume 173, Issue Part 2

• elsevier-article-accepted-2015.pdf