Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
Improved symmetric and nonnegative matrix factorization models for undirected, sparse and large-scaled networks: A triple factorization-based approach
Undirected, sparse and large-scaled networks existing ubiquitously in practical engineering
are vitally important since they usually contain rich information in various patterns. Matrix …
are vitally important since they usually contain rich information in various patterns. Matrix …
Symmetric and nonnegative latent factor models for undirected, high-dimensional, and sparse networks in industrial applications
Undirected, high-dimensional, and sparse (HiDS) networks are frequently encountered in
industrial applications. They contain rich knowledge regarding various useful patterns …
industrial applications. They contain rich knowledge regarding various useful patterns …
Non-negativity constrained missing data estimation for high-dimensional and sparse matrices from industrial applications
High-dimensional and sparse (HiDS) matrices are commonly seen in big-data-related
industrial applications like recommender systems. Latent factor (LF) models have proven to …
industrial applications like recommender systems. Latent factor (LF) models have proven to …
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications
Latent factor (LF) models are highly effective in extracting useful knowledge from High-
Dimensional and Sparse (HiDS) matrices which are commonly seen in various industrial …
Dimensional and Sparse (HiDS) matrices which are commonly seen in various industrial …
An alternating-direction-method of multipliers-incorporated approach to symmetric non-negative latent factor analysis
Large-scale undirected weighted networks are frequently encountered in big-data-related
applications concerning interactions among a large unique set of entities. Such a network …
applications concerning interactions among a large unique set of entities. Such a network …
Robust low-rank latent feature analysis for spatiotemporal signal recovery
Wireless sensor network (WSN) is an emerging and promising developing area in the
intelligent sensing field. Due to various factors like sudden sensors breakdown or saving …
intelligent sensing field. Due to various factors like sudden sensors breakdown or saving …
Spatio-temporal signal recovery based on low rank and differential smoothness
The analysis of spatio-temporal signals plays an important role in various fields including
sociology, climatology, and environmental studies, etc. Due to the abrupt breakdown of the …
sociology, climatology, and environmental studies, etc. Due to the abrupt breakdown of the …
Wireless sensor networks: A big data source in Internet of Things
Background: Devices connected to the internet are increasing day by day, and the era of
Internet of Things (IoT) is anticipated. However, handling big data generated by the IoT …
Internet of Things (IoT) is anticipated. However, handling big data generated by the IoT …
An adaptive latent factor model via particle swarm optimization
Q Wang, S Chen, X Luo - Neurocomputing, 2019 - Elsevier
Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-
Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial …
Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial …