论文标题
大规模无向加权网络的无约束的对称非负潜在因子分析
An Unconstrained Symmetric Nonnegative Latent Factor Analysis for Large-scale Undirected Weighted Networks
论文作者
论文摘要
大规模的无向加权网络通常在与大数据相关的大研究领域中发现。自然可以将其量化为用于实施大数据分析任务的对称高维和不完整（SHDI）矩阵。对称非负潜在因素分析（SNL）模型能够从SHDI矩阵中有效提取潜在因子（LFS）。然而，它依赖于约束培训计划，这使其缺乏灵活性。为了解决这个问题，本文提出了一个不受限制的对称非负潜在因素分析（USNL）模型。它的主要思想是两个方面：1）通过将非负映射函数集成到SNL模型中，输出LFS与决策参数分开； 2）采用随机梯度下降（SGD），用于实施不受限制的模型训练，并确保输出LFS非负性。对由实际的大数据应用产生的四个SHDI矩阵的经验研究表明，与SNL模型相比，USNL模型可以实现缺失数据的预测准确性，并且具有竞争激烈的计算效率。
Large-scale undirected weighted networks are usually found in big data-related research fields. It can naturally be quantified as a symmetric high-dimensional and incomplete (SHDI) matrix for implementing big data analysis tasks. A symmetric non-negative latent-factor-analysis (SNL) model is able to efficiently extract latent factors (LFs) from an SHDI matrix. Yet it relies on a constraint-combination training scheme, which makes it lack flexibility. To address this issue, this paper proposes an unconstrained symmetric nonnegative latent-factor-analysis (USNL) model. Its main idea is two-fold: 1) The output LFs are separated from the decision parameters via integrating a nonnegative mapping function into an SNL model; and 2) Stochastic gradient descent (SGD) is adopted for implementing unconstrained model training along with ensuring the output LFs nonnegativity. Empirical studies on four SHDI matrices generated from real big data applications demonstrate that an USNL model achieves higher prediction accuracy of missing data than an SNL model, as well as highly competitive computational efficiency.
