论文标题
自适应交替方向基于方法的非负潜在因子模型
An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Model
论文作者
论文摘要
基于交替的方法的非负潜在因子模型可以对高维和不完整（HDI）矩阵进行有效的表示学习。但是，它将多个超参数引入学习过程，应谨慎选择其出色的表现。需要进行超参数适应，以进一步增强其可扩展性。针对这个问题，本文提出了一种自适应交替方向方法的非负潜在因子（A2NLF）模型，该模型的超参数适应是按照粒子群优化的原理实施的。对工业应用产生的非负HDI矩阵的实证研究表明，在计算和存储效率方面，A2NLF优于几个最先进的模型，并且在HDI矩阵缺失的数据方面保持了高度竞争性的估计精度。
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.
