Learning low-dimensional nonlinear structures from high-dimensional noisy data: An integral operator approach-Reference-Cited by-同舟云学术

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Learning low-dimensional nonlinear structures from high-dimensional noisy data: An integral operator approach

Published:2023-08-01 Issue:4 Volume:51 Page:
ISSN:0090-5364
Container-title:The Annals of Statistics
language:
Short-container-title:Ann. Statist.

Author:

Ding Xiucai¹,Ma Rong²

Affiliation:

1. Department of Statistics, University of California, Davis

2. Department of Statistics, Stanford University

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference90 articles.

1. Bayati, M. and Montanari, A. (2011). The dynamics of message passing on dense graphs, with applications to compressed sensing. IEEE Trans. Inf. Theory 57 764–785.

2. COIFMAN, R. R. and LAFON, S. (2006). Diffusion maps. Appl. Comput. Harmon. Anal. 21 5–30.

3. BELKIN, M. and NIYOGI, P. (2007). Convergence of Laplacian eigenmaps. Adv. Neural Inf. Process. Syst. 19 129.

4. SINGER, A. (2006). From graph to manifold Laplacian: The convergence rate. Appl. Comput. Harmon. Anal. 21 128–134.

5. SINGER, A. and WU, H.-T. (2017). Spectral convergence of the connection Laplacian from random samples. Inf. Inference 6 58–123.

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