Fast and Efficient Algorithms for Nonnegative Tucker Decomposition

Authors:
Anh Huy Phan;Andrzej Cichocki
Affiliations:
RIKEN Brain Science Institute, Wako-shi, Japan;RIKEN Brain Science Institute, Wako-shi, Japan
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

Citing 4
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Abstract

In this paper, we propose new and efficient algorithms for nonnegative Tucker decomposition (NTD): Fast 茂戮驴-NTD algorithm which is much precise and faster than 茂戮驴-NTD [1]; and β-NTD algorithm based on the βdivergence. 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.