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 …
Learning tensor low-rank representation for hyperspectral anomaly detection
Recently, low-rank representation (LRR) methods have been widely applied for
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
hyperspectral anomaly detection, due to their potentials in separating the backgrounds and …
Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing
Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit
system. Since the metro or other rail transit have high safety requirements, it is hard to …
system. Since the metro or other rail transit have high safety requirements, it is hard to …
Generalized nonconvex low-rank tensor approximation for multi-view subspace clustering
The low-rank tensor representation (LRTR) has become an emerging research direction to
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
boost the multi-view clustering performance. This is because LRTR utilizes not only the …
Infrared dim and small target detection via multiple subspace learning and spatial-temporal patch-tensor model
Robust detection of infrared small and dim targets with highly heterogeneous backgrounds
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …
plays an indispensable role in infrared search and tracking (IRST) system, which is still a …
When Laplacian scale mixture meets three-layer transform: A parametric tensor sparsity for tensor completion
Recently, tensor sparsity modeling has achieved great success in the tensor completion
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
(TC) problem. In real applications, the sparsity of a tensor can be rationally measured by low …
Multiple kernel clustering with kernel k-means coupled graph tensor learning
Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple
kernel clustering (MKC), which have both been widely used to identify clusters that are non …
kernel clustering (MKC), which have both been widely used to identify clusters that are non …
Robust and optimal neighborhood graph learning for multi-view clustering
Y Du, GF Lu, G Ji - Information Sciences, 2023 - Elsevier
In recent years, researchers have proposed many graph-based multi-view clustering (GMC)
algorithms to solve the multi-view clustering (MVC) problem. However, there are still some …
algorithms to solve the multi-view clustering (MVC) problem. However, there are still some …