An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model

TL;DR

This paper introduces A2NLF, an adaptive nonnegative latent factor model that uses particle swarm optimization to tune hyper-parameters, improving efficiency and accuracy in high-dimensional incomplete matrix representations.

Contribution

It proposes an adaptive hyper-parameter tuning method for the nonnegative latent factor model using particle swarm optimization, enhancing scalability and performance.

Findings

A2NLF outperforms state-of-the-art models in efficiency.

A2NLF maintains competitive accuracy in missing data estimation.

The model demonstrates scalability on industrial HDI matrices.

Abstract

An alternating-direction-method-based nonnegative latent factor model can perform efficient representation learning to a high-dimensional and incomplete (HDI) matrix. However, it introduces multiple hyper-parameters into the learning process, which should be chosen with care to enable its superior performance. Its hyper-parameter adaptation is desired for further enhancing its scalability. Targeting at this issue, this paper proposes an Adaptive Alternating-direction-method-based Nonnegative Latent Factor (A2NLF) model, whose hyper-parameter adaptation is implemented following the principle of particle swarm optimization. Empirical studies on nonnegative HDI matrices generated by industrial applications indicate that A2NLF outperforms several state-of-the-art models in terms of computational and storage efficiency, as well as maintains highly competitive estimation accuracy for an HDI matrix's missing data.