Structure preserving embedding

ACM International Conference Proceeding Series

Volumn 382, Issue , 2009, Pages

  Shaw, Blake ^a   Jebara, Tony ^a  

^a Columbia University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONNECTIVITY ALGORITHMS; CONNECTIVITY STRUCTURES; DIMENSIONALITY REDUCTION ALGORITHMS; EUCLIDEAN SPACES; GLOBAL TOPOLOGICAL PROPERTIES; GRAPH EMBEDDINGS; GRAPHS AND NETWORKS; HIGH DIMENSIONAL DATA; INPUT GRAPHS; K-NEAREST NEIGHBORS; KERNEL MATRICES; LAPLACIAN EIGENMAPS; LINEAR INEQUALITIES; LOSSLESS COMPRESSION; SEMIDEFINITE PROGRAMS; STRUCTURE-PRESERVING;

IMAGE COMPRESSION; LINEAR MATRIX INEQUALITIES; ROBOT LEARNING; SOLAR RADIATION; TOPOLOGY; VISUALIZATION;

ALGORITHMS;

EID: 70049118725     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553494     Document Type: Conference Paper

References (16)

1. Adamic, L. A., & Glance, N. (2005). The political blogosphere and the 2004 US election. WWW-2005 Workshop on the Weblogging Ecosystem.
2. Arora, S., Rao, S., & Vazirani, U. (2004). Expander flows, geometric embeddings and graph partitioning. Symposium on Theory of Computing.
3. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. http://www.ics.uci. edu/̃mlearn/MLRepository.html.
4. Battista, G. D., Eades, P., Tamassia, R., & Tollis, I. (1999). Graph drawing: algorithms for the visualization of graphs. Prentice Hall.
5. Belkin, M., & Niyogi, P. (2002). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15, 1373-1396.
6. Burer, S., & Monteiro, R. D. C. (2003). A nonlinear programming algorithm for solving semidefinite programs via low-rank factorization. Mathematical Programming (series B), 95(2), 329-357.
7. Chung, F. R. K. (1997). Spectral graph theory. American Mathematical Society.
8. Cox, T., & M.Cox (1994). Multidimensional scaling. Chapman & Hall.
9. Finley, T., & Joachims, T. (2008). Training structural svms when exact inference is intractable. Proc. of 25th International Conference on Machine Learning (pp. 304-311).
10. Fremuth-Paeger, C., & Jungnickel, D. (1999). Balanced network flows, a unifying framework for design and analysis of matching algorithms. Networks, 33, 1-28.
11. Roweis, S., & Saul, L. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2323-2326.
12. th International Conference on Artificial Intelligence and Statistics (pp. 460-467).
13. Snapp, R., & Venkatesh, S. (1998). Asymptotic expansions of the k nearest neighbor risk. The Annals of Statistics, 26, 850-878.
14. Sontag, D., & Jaakkola, T. (2008). New outer bounds on the marginal polytope. Advances in Neural Information Processing Systems 20 (pp. 1393-1400).
15. Tenenbaum, J., de Silva, V., & Langford, J. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290, 2319-2323.
16. th International Workshop on Artificial Intelligence and Statistics (pp. 381-388).