Computational Methods for Intelligent Information Access
Supercomputing '95 Proceedings of the 1995 ACM/IEEE conference on Supercomputing
Algorithm 805: computation and uses of the semidiscrete matrix decomposition
ACM Transactions on Mathematical Software (TOMS)
Dimension Reduction Methods for Image Retrieval
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Learning Image Components for Object Recognition
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Document clustering using nonnegative matrix factorization
Information Processing and Management: an International Journal
Boolean Factor Analysis by Attractor Neural Network
IEEE Transactions on Neural Networks
Implementing Boolean Matrix Factorization
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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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.