论文标题
比较研究插值分解的推理方法
Comparative Study of Inference Methods for Interpolative Decomposition
论文作者
论文摘要
在本文中，我们提出了一个具有自动相关性测定（ARD）的概率模型，用于学习插值分解（ID），该模型通常用于低级别近似，特征选择和识别数据中的隐藏模式，其中矩阵因子是与每个数据维度相关的潜在变量。在指定子空间上具有支持的先前密度用于解决观察到的矩阵的分量分量的大小的约束。采用了基于Gibbs抽样的贝叶斯推理程序。我们在各种现实世界数据集上评估了该模型，包括CCLE $ EC50 $，CCLE $ IC50 $，基因体甲基化和具有不同尺寸和尺寸的启动子甲基化数据集，并表明提议的贝叶斯ID ID算法与自动相关性确定与较小的重建性错误相比，与Vanilla bayersian Id datrys相比，将其与较小的重建级别的分组相比。
In this paper, we propose a probabilistic model with automatic relevance determination (ARD) for learning interpolative decomposition (ID), which is commonly used for low-rank approximation, feature selection, and identifying hidden patterns in data, where the matrix factors are latent variables associated with each data dimension. Prior densities with support on the specified subspace are used to address the constraint for the magnitude of the factored component of the observed matrix. Bayesian inference procedure based on Gibbs sampling is employed. We evaluate the model on a variety of real-world datasets including CCLE $EC50$, CCLE $IC50$, Gene Body Methylation, and Promoter Methylation datasets with different sizes, and dimensions, and show that the proposed Bayesian ID algorithms with automatic relevance determination lead to smaller reconstructive errors even compared to vanilla Bayesian ID algorithms with fixed latent dimension set to matrix rank.
