Advances in Neural Networks - ISNN 2008

Volume 5264 of the series Lecture Notes in Computer Science pp 772-782

Fast and Efficient Algorithms for Nonnegative Tucker Decomposition

  • Anh Huy PhanAffiliated withRIKEN Brain Science Institute
  • , Andrzej CichockiAffiliated withRIKEN Brain Science Institute

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In this paper, we propose new and efficient algorithms for nonnegative Tucker decomposition (NTD): Fast α-NTD algorithm which is much precise and faster than α-NTD [1]; and β-NTD algorithm based on the β divergence. These new algorithms include efficient normalization and initialization steps which help to reduce considerably the running time and increase dramatically the performance. Moreover, the multilevel NTD scheme is also presented, allowing further improvements (almost perfect reconstruction). The performance was also compared to other well-known algorithms (HONMF, HOOI, ALS algorithms) for synthetic and real-world data as well.


Nonnegative Tucker decomposition (NTD) Nonnegative matrix factorization (NMF) Alpha divergence Beta divergence Hierarchical decomposition