论文标题
贝叶斯非负基质分解的灵活和分层的先验
Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization
论文作者
论文摘要
在本文中，我们介绍了一种用于学习非负矩阵分解（NMF）的概率模型，该模型通常用于预测数据中的缺失值并在数据中找到隐藏的模式，其中矩阵因子是与每个数据维度相关的潜在变量。潜在因素的非负限制是通过在非负子空间的支持下选择先验来处理的。采用了基于Gibbs抽样的贝叶斯推理程序。我们在几个现实世界数据集上评估了该模型，包括Movielens 100K和Movielens 1M具有不同尺寸和尺寸的Movielens，并表明所提出的贝叶斯NMF GRRN模型可提供更好的预测，并避免与现有的贝叶斯NMF方法相比，避免过度适应。
In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent variables associated with each data dimension. The nonnegativity constraint for the latent factors is handled by choosing priors with support on the nonnegative subspace. Bayesian inference procedure based on Gibbs sampling is employed. We evaluate the model on several real-world datasets including MovieLens 100K and MovieLens 1M with different sizes and dimensions and show that the proposed Bayesian NMF GRRN model leads to better predictions and avoids overfitting compared to existing Bayesian NMF approaches.
