[PDF][PDF] A survey of manifold-based learning methods

X Huo, XS Ni, AK Smith - Recent advances in data mining of …, 2007 - cis.upenn.edu
X Huo, XS Ni, AK Smith
Recent advances in data mining of enterprise data, 2007cis.upenn.edu
We review the ideas, algorithms, and numerical performance of manifold-based machine
learning and dimension reduction methods. The representative methods include locally
linear embedding (LLE), ISOMAP, Laplacian eigenmaps, Hessian eigenmaps, local tangent
space alignment (LTSA), and charting. We describe the insights from these developments,
as well as new opportunities for both researchers and practitioners. Potential applications in
image and sensor data are illustrated. This chapter is based on an invited survey …
Abstract
We review the ideas, algorithms, and numerical performance of manifold-based machine learning and dimension reduction methods. The representative methods include locally linear embedding (LLE), ISOMAP, Laplacian eigenmaps, Hessian eigenmaps, local tangent space alignment (LTSA), and charting. We describe the insights from these developments, as well as new opportunities for both researchers and practitioners. Potential applications in image and sensor data are illustrated. This chapter is based on an invited survey presentation that was delivered by Huo at the 2004 INFORMS Annual Meeting, which was held in Denver, CO, USA.
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