Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76631-5_64 | MICAI |
Keywords | Field | DocType |
feature set extraction,cluster analysis method,new neural network method,neural network,artificial data set,feature extraction task,bars problem,boolean factor analysis,boolean sum,interesting result,data structure,cluster analysis,dimension reduction,factor analysis,non negative matrix factorization,feature extraction,singular value decomposition | Singular value decomposition,Data structure,Boolean factor analysis,Dimensionality reduction,Computer science,Matrix decomposition,Feature extraction,Feature set,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISSN | ISBN |
4827 | 0302-9743 | 3-540-76630-8 |
Citations | PageRank | References |
8 | 0.80 | 5 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Václav Snasel | 1 | 1261 | 210.53 |
| Dušan Húsek | 2 | 19 | 3.40 |
| Alexander Frolov | 3 | 17 | 1.92 |
| Hana Rezanková | 4 | 56 | 9.79 |
| Pavel Moravec | 5 | 245 | 23.32 |
| Pavel Polyakov | 6 | 29 | 3.91 |