Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation


  • Changlin Wan Purdue University
  • Wennan Chang Purdue University
  • Tong Zhao Amazon
  • Mengya Li Indiana University
  • Sha Cao Indiana University
  • Chi Zhang Indiana University



Boolean matrix has been used to represent digital information in many fields, including bank transaction, crime records, natural language processing, protein-protein interaction, etc. Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples. Inspired by binary matrix permutation theories and geometric segmentation, we developed a fast and efficient BMF approach, called MEBF (Median Expansion for Boolean Factorization). Overall, MEBF adopted a heuristic approach to locate binary patterns presented as submatrices that are dense in 1's. At each iteration, MEBF permutates the rows and columns such that the permutated matrix is approximately Upper Triangular-Like (UTL) with so-called Simultaneous Consecutive-ones Property (SC1P). The largest submatrix dense in 1 would lie on the upper triangular area of the permutated matrix, and its location was determined based on a geometric segmentation of a triangular. We compared MEBF with other state of the art approaches on data scenarios with different density and noise levels. MEBF demonstrated superior performances in lower reconstruction error, and higher computational efficiency, as well as more accurate density patterns than popular methods such as ASSO, PANDA and Message Passing. We demonstrated the application of MEBF on both binary and non-binary data sets, and revealed its further potential in knowledge retrieving and data denoising.




How to Cite

Wan, C., Chang, W., Zhao, T., Li, M., Cao, S., & Zhang, C. (2020). Fast and Efficient Boolean Matrix Factorization by Geometric Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6086-6093.



AAAI Technical Track: Machine Learning