论文标题
在线性代数和统计模型中编码大型信息结构
Encoding large information structures in linear algebra and statistical models
论文作者
论文摘要
可以对样本和功能中的大小大小进行编码，以加快基于线性代数的统计模型的学习并删除不需要的信号。编码信息可以将样本和特征维度同时降低为较小的表示设置。在这里，在线性混合模型和混合模型上显示了两个示例，这些示例通过用户在尺寸减小方面的选择定义的因素加速了参数估计的运行时间（可以是线性，二次或基于尺寸规范的限制）。
Large information sizes in samples and features can be encoded to speed up the learning of statistical models based on linear algebra and remove unwanted signals. Encoding information can reduce both sample and feature dimension to a smaller representational set. Here two examples are shown on linear mixed models and mixture models speeding up the run time for parameter estimation by a factor defined by the user's choice on dimension reduction (can be linear, quadratic or beyond based on dimension specification).
