Abstract
A typical problem in thermal nondestructive testing/evaluation (TNDT/E) is that of unsupervised feature extraction from the experimental data. Matrix factorization methods (MFMs) are mathematical techniques well suited for this task. In this paper we present the application of three MFMs: principal component analysis (PCA), non-negative matrix factorization (NMF), and archetypal analysis (AA). To better understand the peculiarities of each method the results are first compared on simulated data. It will be shown that the shape of the data set strongly affects the performance. A good understanding of the actual shape of the thermal NDT data is required to properly choose the most suitable MFM, as it is shown in the application to experimental data.
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