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Decomposition of Big Tensors With Low Multilinear Rank
Guoxu Zhou, Andrzej Cichocki, Shengli Xie
Tensor decompositions are promising tools for big data analytics as they bring multiple modes and aspects of data to a unified framework, which allows us to discover complex intern…
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
Qibin Zhao, Guoxu Zhou, Liqing Zhang +2
We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CP-rank ten…
Efficient Nonnegative Tucker Decompositions: Algorithms and Uniqueness
Guoxu Zhou, Andrzej Cichocki, Qibin Zhao +1
Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor dat…
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis
A. Cichocki, D. Mandic, A-H. Phan +4
The widespread use of multi-sensor technology and the emergence of big datasets has highlighted the limitations of standard flat-view matrix models and the necessity to move toward…