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

作者：Malik Zeeshan Khawar^*; Hussain Amir; Wu Jonathan

来源：Neurocomputing, 2016, 173: 127-136.

DOI：10.1016/j.neucom.2014.12.119

• 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.

• 出版日期2016-1-15