| Abstract [eng] |
This work deals with several dimensionality reduction techniques and their implementations in real medical problems. For this reason, firstly, one speaks about classical dimension reduction methods called principal component analysis and singular value decomposition. After these methods are introduced, non – negative matrix factorization (NMF) are presented. Also algorithms for its implementation are introduced. Moreover, two ways for implementation of dimensionality reduction via NMF are presented when applied for feature extraction, followed by pattern recognition. All algorithms were executed using SAS statistical pachage. Patients with heart failure data were used. It was shown that dimensionality reduction could be effective tool for multidimensional data analysis and classification problems. |
