Map Building By Non-linear Dimensionality Reduction of Historical Visibility Data

Author:

Yairi Takehisa1

Affiliation:

1. Research Center for Advanced Science and Technology, University of Tokyo

Publisher

Japanese Society for Artificial Intelligence

Subject

Artificial Intelligence,Software

Reference46 articles.

1. [Belkin 02] Belkin, M. and Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering, in Advances in Neural Information Processing Systems, Vol. 14, pp. 585-591 (2002)

2. Learning Eigenfunctions Links Spectral Embedding and Kernel PCA

3. [Bishop 06] Bishop, C. M.: Pattern Recognition and Machine Learning, Springer (2006)

4. [Borg 97] Borg, I. and Groenen, P.: Modern Multidimensional Scaling: Theory and Applications, Springer (1997)

5. [Burgard 99] Burgard, W., Fox, D., Jans, H., Matenar, C., and Thrun, S.: Sonar-Based Mapping of Large-Scale Mobile Robot Environments using EM, in Proceedings of 16th International Confence on Machine Learning, pp. 67-76 (1999)

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