Projected Hierarchical ALS for Generalized Boolean Matrix Factorization
We introduce a versatile approach for Boolean factorization of binary data matrices based on a projected hierarchical alternating least squares method. The general model considered in this work allows for an arbitrary Boolean combination of the binary rank-1 terms. The underlying approximation probl...
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Published in | ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1 - 5 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
04.06.2023
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Subjects | |
Online Access | Get full text |
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Summary: | We introduce a versatile approach for Boolean factorization of binary data matrices based on a projected hierarchical alternating least squares method. The general model considered in this work allows for an arbitrary Boolean combination of the binary rank-1 terms. The underlying approximation problem is tackled by relaxing the binary constraints and representing the combining function by a multivariate polynomial. This leads to closed-form and simple to implement updates of the alternating algorithm. Performance comparisons with other methods from the literature are presented for the standard Boolean ('OR') mixture model. We also pro-vide results on real data, as well as factorization examples using XOR and 3-term majority logical operators as combining functions. |
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ISSN: | 2379-190X |
DOI: | 10.1109/ICASSP49357.2023.10094568 |