Penalised Reduction & Classification Toolbox
| Authors | |
|---|---|
| Year of publication | 2016 |
| MU Faculty or unit | |
| Web | http://www.iba.muni.cz/index-en.php?pg=research--data-analysis-tools--plda |
| Description | Penalised Reduction & Classification Toolbox provides algorithms for reduction and classification of various types of data, such as genetic data, two-dimensional (2-D) face image data or three-dimensional (3-D) brain image data. The algorithms were implemented as functions in MATLAB environment. Nowadays, the toolbox enables reduction of data by selecting most discriminative features using penalised linear discriminant analysis (pLDA) with resampling, penalised linear regression (pLR) with resampling, and t-test or feature extraction using intersubject principal component analysis (isPCA). The reduced data are then classified into two groups using linear discriminant analysis (LDA) or linear support vector machines (SVM). Classification performance of methods acquired by leave-one-out cross-validation can be compared using the McNemar’s test. |