Towards a Theoretical Foundation for Laplacian-Based Manifold Methods-Reference-Cited by-同舟云学术

Towards a Theoretical Foundation for Laplacian-Based Manifold Methods

Published:2005 Issue: Volume: Page:486-500
ISSN:0302-9743
Container-title:Learning Theory
language:
Short-container-title:

Author:

Belkin Mikhail,Niyogi Partha

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11503415_33.pdf

Reference31 articles.

1. Belkin, M.: Problems of Learning on Manifolds, The University of Chicago, Ph.D. Dissertation (2003)

2. Belkin, M., Niyogi, P.: Using Manifold Structure for Partially Labeled Classification. In: NIPS 2002 (2002)

3. Belkin, M., Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15(6), 1373–1396 (2003)

4. Belkin, M., Niyogi, P., Sindhwani, V.: On Manifold Regularization, AI Stats (2005)

5. Bengio, Y., Paiement, J.-F., Vincent, P., Delalleau, O., Le Roux, N., Ouimet, M.: Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. In: NIPS 2003 (2003)

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