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Found 7 papers, 1 papers with code
Mini-Hes: A Parallelizable Second-order Latent Factor Analysis Model
The performance of an LFA model relies heavily on its training process, which is a non-convex optimization.
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis
Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a linear bias incorporation (LBI) scheme is important in present the training overshooting and fluctuation, as well as preventing the model from premature convergence.
Multi-constrained Symmetric Nonnegative Latent Factor Analysis for Accurately Representing Large-scale Undirected Weighted Networks
An Undirected Weighted Network (UWN) is frequently encountered in a big-data-related application concerning the complex interactions among numerous nodes, e. g., a protein interaction network from a bioinformatics application.
Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks
An Undirected Weighted Network (UWN) is commonly found in big data-related applications.
A Practical Second-order Latent Factor Model via Distributed Particle Swarm Optimization
Determining these hyperparameters is time-consuming and it largely reduces the practicability of an SLF model.
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks
It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks.
An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model
An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix.
