NeCPD: An Online Tensor Decomposition with Optimal Stochastic Gradient Descent

18 Mar 2020 Ali Anaissi Basem Suleiman Seid Miad Zandavi

Multi-way data analysis has become an essential tool for capturing underlying structures in higher-order datasets stored in tensor $\mathcal{X} \in \mathbb{R} ^{I_1 \times \dots \times I_N} $. $CANDECOMP/PARAFAC$ (CP) decomposition has been extensively studied and applied to approximate $\mathcal{X}$ by $N$ loading matrices $A^{(1)}, \dots, A^{(N)}$ where $N$ represents the order of the tensor... (read more)

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