Locally Linear Landmarks for Large-Scale Manifold Learning-Reference-Cited by-同舟云学术

Locally Linear Landmarks for Large-Scale Manifold Learning

Published:2013 Issue: Volume: Page:256-271
ISSN:0302-9743
Container-title:Advanced Information Systems Engineering
language:
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Author:

Vladymyrov Max,Carreira-Perpiñán Miguel Á.

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/978-3-642-40994-3_17

Reference31 articles.

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2. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)

3. Schölkopf, B., Smola, A., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10(5), 1299–1319 (1998)

4. Cox, T.F., Cox, M.A.A.: Multidimensional Scaling. Chapman & Hall, London (1994)

5. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

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