Formal concepts as optimal factors in Boolean factor analysis: Implications and experiments

CEUR Workshop Proceedings

Volumn 331, Issue , 2007, Pages 188-198

  Belohlavek, Radim ^a,b   Vychodil, Vilem ^b  

^a State University of New York at Binghamton   (United States)

^b PALACKÝ UNIVERSITY   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

BOOLEAN MATRICES; BOOLEAN MATRIX; EFFICIENT APPROXIMATION ALGORITHMS; FORMAL CONCEPTS; NUMBER OF FACTORS; SET COVERING PROBLEM; X FACTORS;

APPROXIMATION ALGORITHMS; EXPERIMENTS; FACTOR ANALYSIS; FORMAL CONCEPT ANALYSIS; INFORMATION ANALYSIS; OPTIMIZATION;

MULTIVARIANT ANALYSIS;

EID: 84864072586     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Bartholomew, D. J., Knott M.: Latent Variable Models and Factor Analysis, 2nd Ed., London, Arnold, 1999.
2. Belohlavek R.: Representation of concept lattices by bidirectional associative memories. Neural Computation 12, 10(2000), 2279-2290.
3. Belohlavek R.: Fuzzy Relational Systems: Foundations and Principles. Kluwer, Academic/Plenum Publishers, New York, 2002.
4. Belohlavek R., Vychodil V.: Formal concepts are optimal factors in Boolean factor analysis (submitted). Preliminary version appeared as On Boolean factor analysis with formal concepts as factors. SCIS & ISIS 2006, Int. Conf. Soft Computing and Intelligent Systems & Int. Symposium on Intelligent Systems, Sep 20-24, 2006, Tokyo, Japan, pp. 1054-1059.
5. Carpineto C., Romano G.: Concept Data Analysis. Theory and Applications. J. Wiley, 2004.
6. Cormen T. H., Leiserson C. E., Rivest R. L., Stein C.: Introduction to Algorithms, 2nd Ed. MIT Press, 2001.
7. Cudeck R., MacCallum R. C. (Eds.): Factor Analysis at 100: Historical Developments and Future Directions. Lawrence Erlbaum Associates, Inc., 2007.
8. Frolov A. A., Húsek D., Muraviev I. P., Polyakov P. A.: Boolean factor analysis by Hopfield-like autoassociative memory. IEEE Transactions on Neural Networks Vol. 18, No. 3, May 2007, pp. 698-707. (Pubitemid 46773737)
9. Ganter B., Wille R.: Formal Concept Analysis. Mathematical Foundations. Springer, Berlin, 1999.
10. Harman H. H.: Modern Factor Analysis, 2nd Ed. The Univ. Chicago Press, Chicago, 1970.
11. Keprt A., Snášel V. Binary factor analysis with help of formal concepts. In Proc. CLA 2004, Ostrava, Czech Republic, 2004, pp. 90-101, ISBN 80-248-0597-9.
12. Keprt A., Snášel V.: Binary Factor Analysis with Genetic Algorithms. Proceedings of 4th IEEE WSTST 2005, Muroran, Japan. Springer Verlag, Berlin Heidelberg, Germany, 2005, pp. 1259-1268.
13. McDonald R. P.: Factor Analysis and Related Methods. Lawrence Erlbaum Associates, Inc., 1985.
14. Mickey, M.R., Mundle, P. and Engelman, L.: Boolean factor analysis. In: W.J. Dixon (ed.), BMDP statistical software manual, Vol. 2, 849-860, Berkeley, CA: University of California Press, 1990.
15. Rajapakse R.K, Denham M.: Fast access to concepts in concept lattices via bidirectional associative memories. Neural Computation 17, 10(2005), 2291-2300. (Pubitemid 41186999)
16. Řezanková H., Húsek, D., Frolov, A.A.: Using standard statistical procedures for Boolean factorization. In Proc. SIS 2003, Naples, Italy, 2003, ISBN 88-8399-053-6.
17. Sirota, A. M., and Frolov., A. A., Húsek, D.: Nonlinear factorization in sparsely encoded Hopfield-like neural networks. ESANN European Symposium on Artifical Neural Networks, Bruges, Belgium, 1999, pp. 387-392.
18. Spearman C.: General intelligence, objectively determined and measured. Amer. J. Psychology 15(1904), 201-293.
19. Veiel H.O.: Psychopathology and Boolean factor analysis: a mismatch. Psychological Medicine. Vol. 15 (1985), Issue 3 (Aug), pp. 623-630, ISSN 0033-2917.
20. Wille R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: I. Rival (ed.): Ordered Sets, 445-470, Reidel, Dordrecht-Boston, 1982.