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Binary Orthogonal Non-negative Matrix Factorization

arXiv 2210.10660 ↗First posted 19 Oct 2022cs.LG math.OCcited by 0

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Abstract (arXiv)

We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 35 references that are in the map. First posted on arXiv 19 Oct 2022.

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References · 6

  1. 2014OpenML: networked science in machine learningcited by 526
  2. 2018Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applicationscited by 55
  3. 2015k-Means Clustering Is Matrix Factorizationcited by 11
  4. 2014Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorizationcited by 9
  5. 2020Reverse Annealing for Nonnegative/Binary Matrix Factorizationcited by 8
  6. 2022Semi-supervised Nonnegative Matrix Factorization for Document Classificationcited by 2

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