Parameter Estimation for Non-Gaussian Model-Based Clustering with High-Dimensional Data

大数据是现代数据和统计分析的一个重要问题。计算机可以存储海量数据；然而，准确和快速分析数据的方法没有跟上现代存储技术的进步。在某些情况下，数据在没有经过分析的情况下被丢弃。改进的大数据统计分析将有利于任何处理海量数据的领域，如生物科学(例如基因组学)、金融和信息学、天文学、宇宙学和气候科学。*我提议的研究将利用一种名为进化计算(EC)的计算机编程形式。欧共体使用的技术复制自自然选择进化的生物学理论。在生物学上，目标通常是产生尽可能多的合适的后代，这些后代接着产生他们自己的合适的后代。基因组的随机突变会让一些孩子比他们的父母更健康，或者更不健康。身体健康的孩子更有可能生出健康的后代，所以他们的基因会遗传下去。在我的研究中，“适合度”的衡量标准是算法在大数据集群方面搜索最优解决方案的能力。(分类包括考虑连接数据点的底层结构，以便它们可以被归入正确的组，或正确地标记，例如，将基因表达与癌症类型联系起来。)交叉和变异等技术是从生物学复制而来的，并被用来“进化”算法，使其每次运行时都更适合。*在拟议的研究下，将开发进化算法(EA)，作为几乎无处不在的期望最大化(EM)算法及其变体的替代方案，用于基于高斯和非高斯混合模型的聚类方法。将为混合因素分析模型、混合方差-伽马分布模型和混合方差-伽马因素分析模型开发EAS。其他短期目标包括开发多种比例方差-伽马分布的混合体。这将带来惊人的建模灵活性，同时也保证了集群的凸性--由此产生的组件是超立方体，因此衰减率在每个维度上可能不同。将混合多尺度方差-伽马分布模型推广到混合多尺度方差-伽马因子分析模型，应用于高维数据。然后，将针对多个标度方差-伽马分布的混合模型和多个标度方差-伽马因子分析模型的混合开发EAS，并将其作为交替期望条件最大化算法的替代算法进行研究。

Big data is an important issue for modern data and statistical analysis. Computers can store huge amounts of data; however, methods to accurately and quickly analyze the data have not kept pace with improvements to modern storage technology. In some cases, data are discarded without being analyzed. Improved statistical analysis of big data will benefit any field dealing with massive amounts of data, such as biological sciences (e.g., genomics), finance and informatics, astronomy, cosmology, and climate science.******My proposed research will utilize a form of computer programming called Evolutionary Computation (EC). EC uses techniques copied from the biological theory of evolution by natural selection. In biology, the goal usually is to produce as many fit offspring as possible, who go on to produce their own fit offspring. Random mutations to the genome will make some children more fit, or less fit, than their parents. The fitter children are more likely to produce healthy offspring, so their genes get passed on. For my research, the measure of "fitness" used is how well the algorithm searches for optimum solutions with regard to clustering big data. (Clustering involves accounting for the underlying structure that links data points, so that they can be put into correct groups, or labelled correctly, e.g., linking gene expression to types of cancer.) Techniques such as cross-over and mutation are copied from biology, and are used to "evolve" the algorithm and make it fitter each time it runs. ******Under the proposed research, evolutionary algorithms (EAs) will be developed, as alternatives to the almost ubiquitous expectation-maximization (EM) algorithm and its variants, for Gaussian and non-Gaussian mixture model-based approaches to clustering. EAs will be developed for the mixture of factor analyzers model, the mixture of variance-gamma distributions, and the mixture of variance-gamma factor analyzers models. Other short term objectives include the development of a mixture of multiple scaled variance-gamma distributions. This will bring a phenomenal level of modelling flexibility, while also guaranteeing cluster convexity -- the resulting components are hypercubiods so that the rate of decay can differ in each dimension. The mixture of multiple scaled variance-gamma distributions model will be extended to the mixture of multiple scaled variance-gamma factor analyzers model, for application to high-dimensional data. EAs will then be developed for the mixture of multiple scaled variance-gamma distributions and mixture of multiple scaled variance-gamma factor analyzers models and investigated as alternatives to alternating expectation-conditional maximization algorithms.