A novel approach to large-scale dynamically weighted directed network representation
A d ynamically w eighted d irected n etwork (DWDN) is frequently encountered in various big
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
data-related applications like a terminal interaction pattern analysis system (TIPAS) …
NeuLFT: A novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors
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 …
related application concerning the complex dynamic interactions among numerous entities …
Missing value imputation in multivariate time series with end-to-end generative adversarial networks
Missing values are inherent in multivariate time series because of multiple reasons, such as
collection errors, which deteriorate the performance of follow-up analytic applications on the …
collection errors, which deteriorate the performance of follow-up analytic applications on the …
A survey on tensor techniques and applications in machine learning
This survey gives a comprehensive overview of tensor techniques and applications in
machine learning. Tensor represents higher order statistics. Nowadays, many applications …
machine learning. Tensor represents higher order statistics. Nowadays, many applications …
Fast autoregressive tensor decomposition for online real-time traffic flow prediction
Online real-time traffic flow prediction typically offers better real-time performance than
offline prediction. However, existing studies rarely discussed online real-time traffic flow …
offline prediction. However, existing studies rarely discussed online real-time traffic flow …
Block Hankel tensor ARIMA for multiple short time series forecasting
This work proposes a novel approach for multiple time series forecasting. At first, multi-way
delay embedding transform (MDT) is employed to represent time series as low-rank block …
delay embedding transform (MDT) is employed to represent time series as low-rank block …
Actuator and sensor fault classification for wind turbine systems based on fast Fourier transform and uncorrelated multi-linear principal component analysis techniques
Y Fu, Z Gao, Y Liu, A Zhang, X Yin - Processes, 2020 - mdpi.com
In response to the high demand of the operation reliability and predictive maintenance,
health monitoring and fault diagnosis and classification have been paramount for complex …
health monitoring and fault diagnosis and classification have been paramount for complex …
Incremental Bayesian matrix/tensor learning for structural monitoring data imputation and response forecasting
There has been increased interest in missing sensor data imputation, which is ubiquitous in
the field of structural health monitoring (SHM) due to discontinuous sensing caused by …
the field of structural health monitoring (SHM) due to discontinuous sensing caused by …
Tensor completion for weakly-dependent data on graph for metro passenger flow prediction
Low-rank tensor decomposition and completion have attracted significant interest from
academia given the ubiquity of tensor data. However, low-rank structure is a global property …
academia given the ubiquity of tensor data. However, low-rank structure is a global property …
Low tensor-ring rank completion by parallel matrix factorization
Tensor-ring (TR) decomposition has recently attracted considerable attention in solving the
low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding …
low-rank tensor completion (LRTC) problem. However, due to an unbalanced unfolding …