Large scale tensor factorization via parallel sketches

NeuLFT: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors

X Luo, H Wu, Z Li - IEEE Transactions on Knowledge and Data …, 2022 - ieeexplore.ieee.org
AH igh-D imensional and I ncomplete (HDI) tensor is frequently encountered in a big data-
related application concerning the complex dynamic interactions among numerous entities …
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A multi-aspect neural tensor factorization framework for patent litigation prediction

H Wu, G Zhu, Q Liu, H Zhu, H Wang… - … Transactions on Big …, 2023 - ieeexplore.ieee.org
Patent litigation is an expensive and time-consuming legal process. To reduce costs,
companies can proactively manage patents using predictive analysis to identify potential …
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Multi-relational data characterization by tensors: Perturbation analysis

SY Chang, HC Wu - IEEE Transactions on Knowledge and Data …, 2021 - ieeexplore.ieee.org
Data perturbation is deemed a common problem in data processing. It is often inevitable to
avoid noisy or misleading data which may arise from real-world collection or model …
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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method

Y Ouyang, K ** a novel …
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Partensor: A toolbox for parallel canonical polyadic decomposition

PA Karakasis, C Kolomvakis, G Lourakis… - Tensors for Data …, 2022 - Elsevier
In this chapter, we present efficient algorithms for very large structured canonical polyadic
decomposition and describe their parallel implementation on multicore processing systems …
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