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Reduce before you factorize: A simple scheme for Boolean matrix factorization
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DOI：10.1016/j.knosys.2025.115190.png)
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We propose a simple idea that enables a speed-up of existing algorithms for Boolean matrix factorization. It consists in a straightforward redundancy-removing transformation of the input data and an appropriate modification of the factorization algorithm. Examination of real-world data used for benchmarking reveals that most are amenable to such a transformation, rendering the idea practically significant. Experimental evaluation confirms that our approach results in a significant speed-up of factorization algorithms. We also discuss the implications of our findings for factorization of large Boolean data and outline topics for future research.
Keyword:
Boolean data
Factorization
Redundancy
Reduction
Sampling
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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