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Manifold denoising with Gaussian Process Latent Variable Models

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3 Author(s)
Yan Gao ; Nanyang Technol. Univ., Singapore, Singapore ; Kap Luk Chan ; Wei-Yun Yau

For a finite set of points lying on a lower dimensional manifold embedded in a high-dimensional data space, algorithms have been developed to study the manifold structure. However, many algorithms will fail if data are noisy. We propose a method based on Gaussian process latent variable models for manifold denoising with the following advantages: (1), it is probabilistic, which naturally handles noise and missing data; (2), it works well for very high dimensional data with small sample size; (3), it can recover the low-dimensional submanifolds corrupted by high-dimensional noise; and (4), it deals well with multimodal manifolds.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008