Rodolphe Priam, Mohamed Nadif, Gérard Govaert


The latent block model is an efficient alternative to the mixture model for modelling a dataset when the number of rows or columns of the data matrix studied is large. For analyzing and reducing the spaces of a matrix, the methods proposed in the litterature are most of the time with their foundation in a non-parametric or a mixture model approach. We present an embedding of the projection of co-occurrence tables in the Poisson latent block mixture model. Our approach leads to an efficient way to cluster and reduce this kind of data matrices.


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Paper Citation

in Harvard Style

Priam R., Nadif M. and Govaert G. (2012). NONLINEAR MAPPING BY CONSTRAINED CO-CLUSTERING . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8425-98-0, pages 63-68. DOI: 10.5220/0003764800630068

in Bibtex Style

author={Rodolphe Priam and Mohamed Nadif and Gérard Govaert},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
SN - 978-989-8425-98-0
AU - Priam R.
AU - Nadif M.
AU - Govaert G.
PY - 2012
SP - 63
EP - 68
DO - 10.5220/0003764800630068