期刊
NEUROCOMPUTING
卷 173, 期 -, 页码 127-136出版社
ELSEVIER
DOI: 10.1016/j.neucom.2014.12.119
关键词
Dimensionality reduction; Generalized eigenvalue problem; Laplacian Eigenmaps; Manifold-based learning
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.
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