累积收缩过程下高斯因子模型的变分Bayes方法
Variational Bayes for Gaussian Factor Models under the Cumulative Shrinkage Process
摘要：
累积收缩过程是一个不断增加的收缩先验，可以在模型中使用，其中附加项应该发挥逐渐可忽略的作用。一个自然的应用是高斯因子模型，在这种情况下，这样的过程已被证明是有效的，在诱导简约表示，同时提供准确的推断数据协方差阵。累积收缩过程带有自适应吉布斯采样器，该采样器在整个迭代过程中调整潜在因子的数量，这使得它比非自适应吉布斯采样器更快。在这项工作中，我们提出了一个变分算法的高斯因子模型赋予的累积收缩过程。这种策略提供了与自适应Gibbs采样器相当的推断，并进一步减少了运行时间
英文摘要：
The cumulative shrinkage process is an increasing shrinkage prior that can be employed within models in which additional terms are supposed to play a progressively negligible role. A natural application is to Gaussian factor models, where such a process has proved effective in inducing parsimonious representations while providing accurate inference on the data covariance matrix. The cumulative shrinkage process came with an adaptive Gibbs sampler that tunes the number of latent factors throughout iterations, which makes it faster than the non-adaptive Gibbs sampler. In this work we propose a variational algorithm for Gaussian factor models endowed with a cumulative shrinkage process. Such a strategy provides comparable inference with respect to the adaptive Gibbs sampler and further reduces runtime
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