Name
Abstract | ||
|---|---|---|
In this paper we compare the performance of several dimension reduction techniques which are used as a tool for feature extraction. The tested methods include singular value decomposition, semi-discrete decomposition, non-negative matrix factorization, novel neural network based algorithm for Boolean factor analysis and two cluster analysis methods as well. So called bars problem is used as the benchmark. Set of artificial signals generated as a Boolean sum of given number of bars is analyzed by these methods. Resulting images show that Boolean factor analysis is upmost suitable method for this kind of data. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-69158-7_89 | ICONIP (1) |
Keywords | Field | DocType |
dimension reduction,test methods,singular value decomposition,neural network,feature extraction,cluster analysis,non negative matrix factorization,factor analysis | Singular value decomposition method,Singular value decomposition,Boolean factor analysis,Dimensionality reduction,Pattern recognition,Computer science,Matrix decomposition,Feature extraction,Artificial intelligence,Artificial neural network,Machine learning,Binary number | Conference |
Volume | ISSN | Citations |
4984 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Václav Snasel | 1 | 1261 | 210.53 |
| Pavel Moravec | 2 | 245 | 23.32 |
| Dusan Húsek | 3 | 60 | 11.37 |
| Alexander A. Frolov | 4 | 180 | 29.31 |
| Hana Rezanková | 5 | 56 | 9.79 |
| Pavel Polyakov | 6 | 29 | 3.91 |