Summary Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a low-rank and sparse loadings matrix. The structure of the prior leads to an estimation algorithm that accommodates uncertainty in the number of factors. We propose an information criterion to select the hyperparameters of the prior. Expandable factor analysis has better false discovery rates and true positive rates than its competitors across diverse simulation settings. We apply the proposed approach to a gene expression study of ageing in mice, demonstrating superior results relative to four competing methods.

中文翻译：

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可扩展因子分析

总结 贝叶斯稀疏因子模型已被证明可用于表征多元数据中的相关性，但将计算扩展到大量样本和维度是有问题的。当因子数量未知时，我们为因子模型中的可扩展推理提出了可扩展因子分析。该方法依赖于连续收缩先验，以对低秩和稀疏载荷矩阵进行有效的最大后验估计。先验的结构导致估计算法适应因素数量的不确定性。我们提出了一个信息标准来选择先验的超参数。在不同的模拟设置中，可扩展因子分析具有比其竞争对手更好的错误发现率和真实阳性率。我们将所提出的方法应用于小鼠衰老的基因表达研究，