On the Complexity of Additive Clustering Models

Journal of Mathematical Psychology

Volumn 45, Issue 1, 2001, Pages 131-148

  Lee, Michael D ^a  

^a DEFENCE SCIENCE AND TECHNOLOGY ORGANISATION   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001373780     PISSN: 00222496     EISSN: None     Source Type: Journal    
DOI: 10.1006/jmps.1999.1299     Document Type: Article

References (24)

1. Arabie, P., & Carroll, J. D. (1980). MAPCLUS: A mathematical programming approach to fitting the ADCLUS model. Psychometrika, 45, 211-235.
2. Arabie, P., & Shepard, R. N. (1973). Representation of similarities as combinations of discrete, overlapping properties. Paper presented at Mathematical Psychological Meeting, Montréal.
3. Bellman, R. (1970). Introduction to matrix analysis (second ed.). New York: McGraw-Hill.
4. Chaturvedi, A., & Carroll, J. D. (1994), An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models. Journal of Classification, 11, 155-170.
5. Hojo, H. (1982). A maximum likelihood method for additive clustering and its applications. Japanese Psychological Research, 25(4), 191-201.
6. Horn, J. A., & Johnson, C. R. (1985). Matrix analysis. Cambridge, MA: Cambridge University Press.
7. Kagan, A. M., Linnik, Y. V., & Rao, C. R. (1973). Characterization problems in mathematical statistics. New York: Wiley.
8. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773-795.
9. Kass, R. E., & Wasserman, L. (1996). The selection of prior distributions by formal rules. Journal of the American Statistical Association, 91(435), 1343-1370.
10. Lee, M. D. (1999a). Algorithms for representing similarity data. Technical Report DSTO-RR-0152, Defence Science and Technology Organisation.
11. Lee, M. D. (1999b). An extraction and regularization approach to additive clustering. Journal of Classification, 16, 255-281.
12. Li, M., & Vitányi, P. (1993). An introduction to Kolmogorov complexity and its applications. Reading, MA: Addison-Wesley.
13. Magnus, J. R., & Neudecker, H. (1988). Matrix differential calculus with applications in statistics and econometrics. New York: Wiley.
14. Miller, G. A., & Nicely, P. E. (1955). An analysis of perceptual confusions among some English consonants. Journal of the Acoustical Society of America, 27, 338-352.
15. Mirkin, B. G. (1987). Additive clustering and qualitative factor analysis methods for similarity matrices. Journal of Classification, 4, 7-31.
16. Myung, I. J., & Pitt, M. A. (1997). Applying Occam's razor in modeling cognition: A Bayesian approach. Psychonomic Bulletin & Review, 4, 79-95.
17. Rissanen, J. (1989). Stochastic complexity in statistical inquiry. Singapore: World Scientific.
18. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
19. Shannon, C. E. (1948). The mathematical theory of communication. Bell Systems Technical Journal, 27, 379-243, 623-656.
20. Shepard, R. N. (1972). Psychological representation of speech sounds. In E. E. David & P. B. Denes (Eds.), Human communication: A unified view (pp. 67-113). New York: McGraw-Hill.
21. Shepard, R. N. (1974). Representation of structure in similarity data: Problems and prospects. Psychometrika 39, 373-422.
22. Shepard, R. N. & Arabie, P. (1979). Additive clustering representations of similarities as combinations of discrete overlapping properties. Psychological Review, 86, 87-123.
23. Tenenbaum, J. B. (1996). Learning the structure of similarity. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems (Vol. 8, pp. 3-9). Cambridge, MA: MIT Press.
24. Zemel, R. S. (1995). Minimum description length analysis. In M. A. Arbib (Ed.), Handbook of brain theory and neural networks (pp. 572-575). Cambridge, MA: MIT Press.