An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model
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.
Cite
@article{arxiv.2204.04843,
title = {An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model},
author = {Yurong Zhong and Xin Luo},
journal= {arXiv preprint arXiv:2204.04843},
year = {2022}
}