A fast non-negative latent factor model based on generalized momentum method
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
Convergence analysis of single latent factor-dependent, nonnegative, and multiplicative update-based nonnegative latent factor models
A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU)
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …
learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a …
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 …
Generalized Nesterov's Acceleration-Incorporated, Non-Negative and Adaptive Latent Factor Analysis
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …
A novel approach to extracting non-negative latent factors from non-negative big sparse matrices
An inherently non-negative latent factor model is proposed to extract non-negative latent
factors from non-negative big sparse matrices efficiently and effectively. A single-element …
factors from non-negative big sparse matrices efficiently and effectively. A single-element …
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 …
Efficient extraction of non-negative latent factors from high-dimensional and sparse matrices in industrial applications
High-dimensional and sparse (HiDS) matrices are commonly encountered in many big data-
related industrial applications like recommender systems. When acquiring useful patterns …
related industrial applications like recommender systems. When acquiring useful patterns …
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 …
An instance-frequency-weighted regularization scheme for non-negative latent factor analysis on high-dimensional and sparse data
High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly
seen in industrial applications, such as recommender systems. They can be modeled into an …
seen in industrial applications, such as recommender systems. They can be modeled into an …