Nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, existing algorithms suffer from slow convergence caused by the nonnegativity constraint and hence their practical applications are severely limited. By combining accelerated proximal gradient and low-rank approximation, we propose a new NTF algorithm which is significantly faster than state-of-the-art NTF algorithms.
CITATION STYLE
Zhou, G., Zhao, Q., Zhang, Y., & Cichocki, A. (2014). Fast nonnegative tensor factorization by using accelerated proximal gradient. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8866, pp. 459–468). Springer Verlag. https://doi.org/10.1007/978-3-319-12436-0_51
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