Determining Intrinsic Dimension and Entropy of High-Dimensional Shape Spaces

Author:

Costa Jose A.,Hero Alfred O.

Publisher

Birkhäuser Boston

Reference23 articles.

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

2. M. Bernstein, V. de Silva, J.C. Langford, and J.B. Tenenbaum. Graph approx-imations to geodesics on embedded manifolds. Technical report, Department of Psychology, Stanford University, Palo Alto, CA, 2000.

3. F. Camastra and A. Vinciarelli. Estimating the intrinsic dimension of data with a fractal-based method. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(10):1404–1407, October 2002.

4. M. Carmo. Riemannian Geometry. Birkhäuser, Boston, 1992.

5. J.A. Costa and A.O. Hero. Entropic graphs for manifold learning. In Pro-ceedings of the IEEE Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2003.

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