大规模数据分析的数学和统计观点
Algorithmic and Statistical Perspectives on Large-Scale Data Analysis
摘要：
近年来，来自统计学和科学计算的思想已经开始以越来越复杂和富有成效的方式与来自计算机科学的思想和算法理论相互作用，以帮助开发用于大规模科学和互联网数据分析问题的改进的最坏情况算法。在本章中，我将描述两个最近的例子-一个是从一个（DNA单核苷酸多态性）数据矩阵，另一个是从数据图中选择好的集群或社区（代表社会或信息网络）-这两个领域的想法，并可能作为一个模型，利用互补的算法和统计的角度，以解决应用大-规模数据分析问题。
英文摘要：
In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
