Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence

  title={Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence},
  author={Masahiro Nakano and Hirokazu Kameoka and Jonathan Le Roux and Yu. Kitano and Nobutaka Ono and Shigeki Sagayama},
  journal={2010 IEEE International Workshop on Machine Learning for Signal Processing},
This paper presents a new multiplicative algorithm for nonnegative matrix factorization with β-divergence. The derived update rules have a similar form to those of the conventional multiplicative algorithm, only differing through the presence of an exponent term depending on β. The convergence is theoretically proven for any real-valued β based on the auxiliary function method. The convergence speed is experimentally investigated in comparison with previous works. 
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