偏态双线性因子分析器的简约混合模型
Parsimonious Mixtures of Skewed Bilinear Factor Analyzers
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中文总结 AI 辅助
研究针对聚类偏态随机矩阵的混合模型过参数化问题，提出偏态矩阵变量双线性因子分析器的简约混合模型家族，用AECM算法估计参数，并通过模拟及MNIST和Olivetti人脸数据集考量该方法。
中文摘要 AI 辅助
聚类偏态随机矩阵的混合模型在未进行降维时常常存在过参数化问题。即便使用双线性因子分析器，通过对聚类参数进行约束可进一步减少参数。本文针对偏态矩阵变量双线性因子分析器的混合模型提出了256个简约模型家族，特别是在偏态t分布的情况下。详细讨论了用于参数估计的AECM算法。此外，还进行了广泛的模拟，并在MNIST数据集和Olivetti人脸数据集的情况下对该方法进行了考量。
英文摘要
Mixture models which cluster skewed random matrices can often suffer from over-parameterization in the absence of performing dimension reduction. Even with the use of bilinear factor analyzers, further parameter reduction can be achieved by constraining parameters over clusters. In this manuscript propose a parsimonious family of 256 models for mixtures of skewed matrix variate bilinear factor analyzers, specifically in the case of the skew t distribution. An AECM algorithm for parameter estimation is discussed in detail. Further, extensive simulations are performed, and the method is considered in the case of the MNIST dataset and the Olivetti faces dataset.
