Electronic Journal of Statistics

A scale-based approach to finding effective dimensionality in manifold learning

Xiaohui Wang and J. S. Marron

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The discovering of low-dimensional manifolds in high-dimensional data is one of the main goals in manifold learning. We propose a new approach to identify the effective dimension (intrinsic dimension) of low-dimensional manifolds. The scale space viewpoint is the key to our approach enabling us to meet the challenge of noisy data. Our approach finds the effective dimensionality of the data over all scale without any prior knowledge. It has better performance compared with other methods especially in the presence of relatively large noise and is computationally efficient.

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Electron. J. Statist., Volume 2 (2008), 127-148.

First available in Project Euclid: 17 March 2008

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manifold learning intrinsic dimension scale space hypothesis test multivariate analysis


Wang, Xiaohui; Marron, J. S. A scale-based approach to finding effective dimensionality in manifold learning. Electron. J. Statist. 2 (2008), 127--148. doi:10.1214/07-EJS137. https://projecteuclid.org/euclid.ejs/1205761031

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