Machine Learning Model for Sparse PCM Completion
Abstract
In this paper, we propose a machine learning model for sparse pairwise comparison matrices (PCMs), combining classical PCM approaches with graph-based learning techniques. Numerical results are provided to demonstrate the effectiveness and scalability of the proposed method.
Fields of Study & Subjects
Multi-Criteria Decision Making
Game Theory and Voting Systems
Matrix Theory and Algorithms
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 7 January 2026
- Document type
- Preprint