Abstract
We face the problem of interpreting parts of a dataset as small selections of features. Particularly, we propose a novel masked nonnegative matrix factorization algorithm which is used either to explain data as a composition of interpretable parts (which are actually hidden in them) and to introduce knowledge in the factorization process. Numerical examples prove the effectiveness of the proposed algorithm as a useful tool for Intelligent Data Analysis.
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Casalino, G., Del Buono, N., Mencar, C. (2014). Part-Based Data Analysis with Masked Non-negative Matrix Factorization. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham. https://doi.org/10.1007/978-3-319-09153-2_33
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DOI: https://doi.org/10.1007/978-3-319-09153-2_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09152-5
Online ISBN: 978-3-319-09153-2
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