Jan 12, 2021 · Abstract: A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data ...
A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal patterns hidden in multichannel data emerging from various ...
Abstract— A nonnegative latent factorization of tensors. (NLFT) model precisely represents the temporal patterns hid- den in multichannel data emerging from ...
Zhou, “Adjusting learning depth in nonnegative latent factorization of tensors for accurately modeling temporal patterns in dynamic QoS data,” IEEE ...
The latent factorization of tensors (LFT) develops an efficient way to fulfill the prediction for dynamic quality of service (QoS) data and it has gained ...
Missing: Adjusting Depth
Dynamic Quality-of-Service (QoS) data can be efficiently represented by a Non-negative Latent-factorization-of-tensors model, which relies on a Non-negative and ...
A fast representation learning model for a high-dimensional and incomplete dynamic QoS tensor is proposed that is superior to the state-of-the-art models in ...
Zhou, “Adjusting Learning Depth in Non-negative Latent Factorization of Tensors for Accurately. Modeling Temporal Patterns in Dynamic QoS Data,” IEEE ...
Mar 8, 2023 · ... Adjusting learning depth in non-negative latent factorization of tensors for accurately modeling temporal patterns in dynamic QoS data. IEEE ...
Aug 14, 2022 · A nonnegative latent factorization of tensors (NLFT) model can well model the temporal pattern hidden in nonnegative quality-of-service ...