Document space dimension reduction by nonlinear Hebbian neural network

This paper deals with information retrieval of text documents, and their clustering into some other feature space. The aim of this paper is to reduce the dimension of the document space by the nonlinear Hebbian neural network. As can be seen from the results, not only dimension reduction of document space is made, but also clustering of these documents into clusters. We used here the nonlinear Hebbian neural network, which is feed-forward neural network with unsupervised learning.

[1]  I. Mokris,et al.  Proposal of cascade neural network model for text document space dimension reduction by latent semantic indexing , 2008, 2008 6th International Symposium on Applied Machine Intelligence and Informatics.

[2]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[3]  H. Bourlard,et al.  Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.

[4]  Mark D. Plumbley,et al.  BLIND SEPARATION OF POSITIVE SOURCES USING NON-NEGATIVE PC A , 2003 .

[5]  Erkki Oja,et al.  The nonlinear PCA criterion in blind source separation: Relations with other approaches , 1998, Neurocomputing.

[6]  Christopher M. Bishop,et al.  Proceedings International Conference on Artificial Neural Networks ICANN'95 , 1995 .

[7]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[8]  Michal Laclavík,et al.  1 st Workshop on Intelligent and Knowledge oriented Technologies , 2006 .

[9]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[10]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[11]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[12]  Daniel Memmi,et al.  Neural dimensionality reduction for document processing , 2002, ESANN.

[13]  Andreas Rauber,et al.  Uncovering hierarchical structure in data using the growing hierarchical self-organizing map , 2002, Neurocomputing.

[14]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[15]  A. Rauber,et al.  Document Classification with Unsupervised Artificial Neural Networks , 2000 .

[16]  E. Oja Simplified neuron model as a principal component analyzer , 1982, Journal of mathematical biology.