Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise) non-negative matrix into the product of two smaller-sized nonnegative matrices, which has been shown intractable in general. In order to overcome this issue, separability assumption is introduced which assumes all data points are in a conical hull. This assumption makes NMF tractable and is widely used in text analysis and image processing, but still impractical for huge-scale datasets. In this paper, inspired by recent development on dequantizing techniques, we propose a new classical algorithm for separable NMF problem. Our new algorithm runs in polynomial time in the rank and logarithmic in the size of input matrices, which achieves an exponential speedup in the low-rank setting.

International Joint Conference on Artificial Intelligence
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Chen, Z, Li, Y, Sun, X, Yuan, P, & Zhang, J. (2019). A quantum-inspired classical algorithm for separable Non-negative Matrix Factorization. In Proceedings of the International Conference on Artificial Intelligence (pp. 4511–4517).