The PRIMPING routine: tiling through proximal alternating linearized minimization

Sibylle Hess (Corresponding author), Katharina Morik, Nico Piatkowski

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Scopus)
107 Downloads (Pure)


Mining and exploring databases should provide users with knowledge and new insights.
Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise. We propose a novel Boolean matrix factorization algorithm to solve the tiling problem, based on recent results from optimization theory. In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization. This approach is well known for model selection and data compression, but not for finding suitable factorizations via numerical optimization. We demonstrate the superior robustness of the new approach in the presence of several kinds of noise and types of underlying structure. Moreover, our general framework can work with any cost measure having a suitable real-valued relaxation. Thereby, no convexity assumptions have to be met.
The experimental results on synthetic data and image data show that the new method identifies interpretable patterns which explain the data almost always better than the competing algorithms.
Original languageEnglish
Pages (from-to)1090-1131
Number of pages42
JournalData Mining and Knowledge Discovery
Issue number4
Publication statusPublished - 1 Jul 2017
Externally publishedYes


  • Alternating minimization
  • Boolean matrix factorization
  • Minimum description length principle
  • Nonconvex-nonsmooth minimization
  • Proximal alternating linearized minimization
  • Tiling


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