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Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data.
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G. Zhou, A. Cichocki, Q. Zhao, and S. Xie, “Nonnegative matrix and tensor factorizations: An algorithmic perspective,” IEEE Signal Processing Magazine , vol. 31, no. 3, pp. 54–65, May 2014
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Z. Li, J. Liu, J. Tang, and H. Lu, “Robust structured subspace learning for data representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. PP, no. 99, pp. 1–1, 2015
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G. Zhou, Q. Zhao, Y. Zhang, T. Adali, and A. Cichocki, “Linked component analysis from matrices to high order tensors: Applications to biomedical data,” Proceedings of the IEEE , 2015. Accepted
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