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

NEUROCOMPUTING

卷:173

Article

Malik, Zeeshan Khawar¹  Hussain, Amir¹  Wu, Jonathan² 

[1] Univ Stirling, Stirling FK9 4LA, Scotland

[2] Univ Windsor, Dept Elect & Comp Engn, Windsor, ON N9B 3P4, Canada

关键词: Dimensionality reduction;    Generalized eigenvalue problem;    Laplacian Eigenmaps;    Manifold-based learning;   

DOI  :  10.1016/j.neucom.2014.12.119

来源: Elsevier

【 摘 要 】

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. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

Free