Abstract
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.
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Zhou, G., Zhao, Q., Zhang, Y., Cichocki, A. (2014). Fast Nonnegative Tensor Factorization by Using Accelerated Proximal Gradient. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_51
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DOI: https://doi.org/10.1007/978-3-319-12436-0_51
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