Robust euclidean embedding

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 169-176

  Cayton, Lawrence ^a   Dasgupta, Sanjoy ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSICAL MULTIDIMENSIONAL SCALING; ROBUST ALGORITHM; ROBUST EUCLIDEAN EMBEDDING; SEMIDEFINITE PROGRAMMING;

ALGORITHMS; SCALING LAWS; STORAGE ALLOCATION (COMPUTER);

COMPUTER PROGRAMMING;

EID: 34250727581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143866     Document Type: Conference Paper

References (15)

1. Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. IEEE Pattern Anal. Mach. Intell., 24.
2. Bertsekas, D. P. (1999). Nonlinear progamming. Athena Scientific. 2nd edition.
3. Borg, I., & Groenen, P. (2005). Modern multidimensional scaling. Springer. 2nd edition.
4. Dattorro, J. (2005). Convex optimization and euclidean distance geometry. Meboo publishing.
5. de Silva, V., &: Tenenbaum, J. (2004). Sparse multidimensional scaling using landmark points. Manuscript.
6. Dhamdhere, K., Gupta, A., & Ravi, R. (2004). Approximation algorithms for minimizing average distortion. Proc. 21st STACS.
7. Gaffke, N., & Mathar, R. (1989). A cyclic projection algorithm via, duality. Metrika, 36, 29-54.
8. Glunt, W., Hayden, L., Hong, S., & Wells, J. (1990). An alternating projection algorithm for computing the nearest euclidean distance matrix. SIAM J. Matrix Anal. Appl., 11, 589-600.
9. Håstad, J., Ivansson, L., & Lagergren, J. (1998). Fittings points on the real line and its application to rh mapping. ESA (pp. 465-476).
10. Huber, P. (1981). Robust statistics. Wiley Interscience.
11. Kruskal, J., & Wish, M. (1978). Multidimensional scaling. Sage Publications.
12. Linial, N., London, E., & Rabinovich, Y. (1995). The geometry of graphs and some of its algorithmic applications. Combinatorica, 15, 215-245.
13. Platt, J. (2005). Fastmap, metricmap, and landmark mds are all nyström algorithms. Proc. 10th AISTATS.
14. Saxe, J. (1979). Embeddability of weighted graphs in k-space is strongly np-hard. Proc. Allerton Conference on Circuit and System theory.
15. Toh, K. C., Todd, M. J., & Tutuncu, R. (1999). SDPT3 - a Matlab software package for semidefinite programming. Opt. Methods and Software, 11.