Supervised multidimensional scaling for visualization, classification, and bipartite ranking

Computational Statistics and Data Analysis

Volumn 55, Issue 1, 2011, Pages 789-801

  Witten, Daniela M ^a   Tibshirani, Robert ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Classification; Majorization; Multidimensional scaling; Ranking; Unidimensional scaling; Unsupervised learning

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA HANDLING; DISEASES; GENE EXPRESSION; UNSUPERVISED LEARNING;

DISSIMILARITY MATRIX; EUCLIDEAN DISTANCE; HANDWRITTEN DIGIT; MAJORIZATION; MULTI-DIMENSIONAL SCALING; RANKING; SIMULATION STUDIES; UNIDIMENSIONAL SCALING;

LEAST SQUARES APPROXIMATIONS;

EID: 77958051425     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2010.07.001     Document Type: Article

References (18)

1. Agarwal, S., Graepel, T., Herbrich, R., Har-Peled, S., Roth, D., 2005. Generalization bounds for the area under the ROC curve. Journal of Machine Learning Research 6, 393-425.
2. Agarwal, S., Niyogi, P., 2009. Generalization bounds for ranking algorithms via algorithmic stability. Journal of Machine Learning Research 10, 441-474.
3. Borg, I., Groenen, P., 2005. Modern Multidimensional Scaling. Springer, New York.
4. Buja, A., Swayne, D., Littman, M., Dean, N., Hofmann, H., Chen, L., 2008. Data visualization with multidimensional scaling. Journal of Computational and Graphical Statistics 17, 444-472.
5. Cohen, W., Schapire, R., Singer, Y., 1998. Learning to order things. In: NIPS 1997. vol. 10. pp. 451-457.
6. Cox, T., Cox, M., 1994. Multidimensional Scaling. Chapman and Hall, London.
7. Cox, T., Ferry, G., 1993. Discriminant analysis using non-metric multidimensional scaling. Pattern Recognition 26, 145-153.
8. Crammer, K., Singer, Y., 2001. Pranking with ranking. In: NIPS 2001. vol. 14. pp. 641-647.
9. Freund, Y., Iyer, R., Schapire, R., Singer, Y., 2003. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research 4, 933-969.
10. Green, P., 1989. Multidimensional Scaling: Concepts and Applications. Allyn and Bacon, Needham Heights, Mass.
11. Hastie, T., Simard, P., 1998. Models and metrics for handwritten digit recognition. Statistical Science 13, 54-65.
12. Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning; Data Mining, Inference and Prediction. Springer Verlag, New York.
13. Kruskal, J., 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 1-27.
14. Park, M.Y., Hastie, T., 2007. An L1 regularization path algorithm for generalized linear models. Journal of the Royal Statistical Society. Series B 69 (4), 659-677.
15. Rifkin, R., Klautau, A., 2004. In defense of one-vs-all classification. Journal of Machine Learning Research 5, 101-141.
16. Shepard, R., 1962. The analysis of proximities: multidimensional scaling with an unknown distance function. Psychometrika 27, 125-140.
17. Simard, P., Cun, Y.L., Denker, J., 1993. Efficient pattern recognition using a new transformation distance. In: Advances in Neural Information Processing Systems. Morgan Kaufman, San Mateo, CA, pp. 50-58.
18. Singh, D., Febbo, P.G., Ross, K., Jackson, D.G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A.A., D'Amico, A.V., Richie, J.P., Lander, E.S., Loda, M., Kantoff, P.W., Golub, T.R., Sellers, W.R., 2002. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203-20