Abstract
Bars problem is widely used as a benchmark for the class of feature extraction tasks. In this model, artificial data set is generated as a Boolean sum of a given number of bars. We show that the most suitable technique for feature set extraction in this case is neural network based Boolean factor analysis. Results are confronted with several dimension reduction techniques. These are singular value decomposition, semi-discrete decomposition and non-negative matrix factorization. Even if these methods are linear, it is interesting to compare them with neural network attempt, because they are well elaborated and are often used for a similar tasks. We show that frequently used cluster analysis methods can bring interesting results, at least for first insight to the data structure.
The work was partly funded by the Center of Applied Cybernetics 1M6840070004 and partly by the Institutional Research Plan AV0Z10300504 ”Computer Science for the Information Society: Models, Algorithms, Applications”, by the project 1ET100300414 of the Program Information Society of the Thematic Program II of the National Research Program of the Czech Republic and by the project 201/05/0079 of the Grant Agency of the Czech Republic.
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Snášel, V., Húsek, D., Frolov, A., Řezanková, H., Moravec, P., Polyakov, P. (2007). Bars Problem Solving - New Neural Network Method and Comparison. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_64
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DOI: https://doi.org/10.1007/978-3-540-76631-5_64
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