Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data

Proceedings of the National Academy of Sciences of the United States of America

Volumn 100, Issue 10, 2003, Pages 5591-5596

  Donoho, David L ^a   Grimes, Carrie ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

ISOMAP; Isometry; Laplacian eigenmaps; Manifold learning; Tangent coordinates

Indexed keywords

ALGORITHM; ARTICLE; ISOMETRY; LAPLACIAN EIGENMAP; MANIFOLD LEARNING; PRIORITY JOURNAL; STATISTICAL ANALYSIS; TANGENT COORDINATE;

EID: 0037948870     PISSN: 00278424     EISSN: None     Source Type: Journal    
DOI: 10.1073/pnas.1031596100     Document Type: Article

References (6)

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2. Donoho, D. L. & Grimes, C. (2002) Technical Report 2002-27 (Dept. of Statistics, Stanford University, Stanford, CA).
3. Donoho, D. L. & Grimes, C. (2002) Proceedings of the European Symposium on Artificial Neural Networks (d-side, Evere, Belgium), Vol. 10, pp. 199-204.
4. Roweis, S. T. & Saul, L. K. (2000) Science, 290, 2323-2326.
5. Belkin, M. & Niyogi, P. (2002) in Advances in Neural information Processing Systems. eds. Dietterich, T. G., Becker, S. & Ghahramani, Z. (MIT Press, Cambridge, MA), Vol. 14, pp. 585-591.
6. Do Carmo, M. P. (1992) in Riemannian Geometry, eds. Kadison, R. V. & Singer, I. M. (Birkhäuser, Boston), pp. 124-143.