Latent variable models for the topographic organisation of discrete and strictly positive data

Neurocomputing

Volumn 48, Issue 1-4, 2002, Pages 185-198

  Girolami, Mark ^a  

^a UNIVERSITY OF THE WEST OF SCOTLAND   (United Kingdom)

Author keywords

Generative models; Latent semantic analysis; Non negative matrix factorisation; Topographic mappings

Indexed keywords

MATHEMATICAL MODELS; MATRIX ALGEBRA; PROGRAM DOCUMENTATION; VECTORS;

TOPOGRAPHIC ORGANIZATION;

DATA REDUCTION;

CONTROLLED STUDY; DATA ANALYSIS; DOCUMENTATION; INFORMATION PROCESSING; LEARNING; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; METHODOLOGY; MULTIVARIATE ANALYSIS; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROBABILITY; REVIEW; TOPOGRAPHY;

EID: 0036825785     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(01)00659-2     Document Type: Review

References (14)

1. C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, 1995.
2. C.M. Bishop, M. Svensén, C.K.I. Williams, GTM: the generative topographic mapping, Neural Comput. 10 (1) (1998) 215-234.
3. S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landatter, R. Harshman, Indexing by latent semantic analysis, J. Amer. Soc. Inform. Sci. 41 (1990) 391-407.
4. M. Girolami, A. Cichocki, S.I. Amari, A common neural network model for exploratory data analysis and independent component analysis, IEEE Trans. Neural Networks 9 (6) (1998) 1495-1501.
5. B.L. Isbell, P. Viola, Restructuring sparse high dimensional data for effective retrieval, Advances Neural Inform. Process. Systems 11 (1999) 480-486.
6. M. Juvela, K. Lehtinen, P. Paatero, The use of positive matrix factorisation in the analysis of molecular line spectra, Roy. Astronom. Soc. 280 (1996) 616-626.
7. A. Kaban, M. Girolami, Initialised and guided EM-clustering of sparse binary data with applications to text based documents, Proc, ICPR'2000, International Conference on Pattern Recognition, Vol. 2, 2000, pp. 748-751.
8. A. Kaban, M. Girolami, A combined latent class and trait model for the analysis and visualisation of discrete data, IEEE Trans. Pattern Anal. Mach. Intell. 23 (2001).
9. T. Kohonen, Self-Organising Maps, Springer, Berlin, 1995.
10. D. Lee, S. Seung, Learning the parts of objects by non-negative matrix factorisation, Nature 401 (1999) 788-791.
11. D. Lee, S. Seung, Algorithms for non-negative matrix factorisation, Neural Inform. Process. Systems (2000), to appear.
12. A. McCallum, K. Nigam, A comparison of event models for naive Bayes text classification, AAAI-98 Workshop on Learning for Text Categorisation, http://www.cs.cmu.edu/ mccallum, 1998.
13. P. McCullagh, J.A. Nelder, Generalised Linear Models, Chapman & Hall, London, 1985.
14. M.E. Tipping, Probabilistic visualisation of high-dimensional binary data, NIPS 11 (1999) 592-598.