Mixtures of Contaminated Shifted Asymmetric Laplace Factor Analyzers

受污染的移位不对称拉普拉斯因子分析仪的混合物

• 批准号: RGPIN-2017-04676
• 负责人: Franczak, Brian
• 金额: $ 1.02万
• 依托单位: Grant MacEwan University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636042
• 关键词: Mixtures; Contaminated; Shifted; Asymmetric; Laplace

Cluster analysis can be defined as the search for interesting groups within data. When a finite mixture model is used for cluster analysis, it is called model-based clustering. Typically, the development of novel model-based clustering approaches has focused on the Gaussian mixture model. Unfortunately, the assumption that the subpopulations of the observed data are Gaussian distributed is ofttimes unrealistic. The research proposed herein will extend the current literature on model-based clustering via development of finite mixtures of non-Gaussian distributions. Specifically, a mixture of contaminated shifted asymmetric Laplace factor analyzers (MCSALFA) will be developed. This model will be well suited for the analysis of high-dimensional and big data that contain spurious, outlying, or noisy observations. Of special interest are data sets whose number of variables exceeds the number of observations, like those arising from microarray gene expression analysis.

聚类分析可以定义为在数据中搜索感兴趣的组。当有限混合模型用于聚类分析时，它被称为基于模型的聚类。通常，基于模型的聚类方法的发展主要集中在高斯混合模型上。不幸的是，假设观测数据的子群体是高斯分布的往往是不现实的。本文提出的研究将扩展目前的文献基于模型的聚类通过发展有限的混合非高斯分布。具体而言，将开发一种混合污染的移位不对称拉普拉斯因子分析仪（MCSALFA）。该模型将非常适合于分析包含虚假，外围或噪声观测的高维和大数据。特别令人感兴趣的是数据集的变量的数量超过了观察的数量，如从微阵列基因表达分析产生的那些。