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Learning dictionaries for sparse signal representation
 Ettl, Ondřej ; Jirgl, Miroslav (referee) ; Mihálik, Ondrej (advisor)
This thesis focuses on examining data from measurements on a pressure mattress that uses a 30 × 11. sensor grid to collect. This data will be used to train and test learners dictionaries that are built using sparse signal representation. Applied learning methods include the method of optimal direction (MOD) and K-SVD, which uses singular decomposition. The resulting dictionaries for different numbers of iterations or atoms are then used to classify and reconstruction of the test data. Cross-validation determined the true positive ratio of the models, which was then compared with conventional classifiers. These models included Decision trees, KNNs and SVMs. Finally, the ability of the learning dictionaries was verified to filter the error in the corrupted image.
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