Kernels and ensembles for horizontal and vertical information selection

用于水平和垂直信息选择的内核和集成

• 批准号: 250419-2011
• 负责人: Zhu, Mu
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570916
• 关键词: Kernels; ensembles; horizontal; vertical; information

Exemplified by the support vector machine and the AdaBoost algorithm, kernel methods and ensemble methods constituted much of the new research in statistical machine learning during the last decade. My proposed research will focus on the development of these two classes of methods for two very general data-analytic problems, which I call horizontal and vertical information selection. Suppose we are given a data matrix, where the rows are observations and the columns are variables. By "vertical" selection, I mean the selection of important rows. In applications, these rows can be "bad guys" (e.g., fraudulent financial transactions) or "good guys" (e.g., chemical molecules with potential to inhibit a certain virus). By "horizontal" selection, I mean the selection of important columns. In applications, these columns are the most important factors that affect the outcome of interest (e.g., biomarkers for detecting a certain disease, or genes associated with a certain cancer). Due to the very general nature of these selection problems, my proposed research is expected to have broad practical implications. For example, a general paradigm for constructing very efficient kernel machines for vertical selection can benefit a wide spectrum of applied research, ranging from fraud detection to drug discovery. I will be studying new ideas that I have proposed, such as "deep kernel machines" (DKMs) and "variable-selection ensembles" (VSEs), as well as establishing an Analytics and Informatics Laboratory (AI Lab; http://www.math.uwaterloo.ca/~ailab). Deeper understandings of DKMs and VSEs will further advance the field of statistical machine learning. The AI Lab will provide a platform for students to receive training and conduct research in machine learning and data mining by creating data-driven applications on the internet, such as recommendation systems. These systems are closely related to the theme of my proposal. They analyze data matrices whose columns are items and whose rows are users, and the objective is to recommend a few items to each user, or to collectively solve a horizontal selection problem for each fixed vertical dimension.

以支持向量机和AdaBoost算法为例，核方法和集成方法构成了过去十年中统计机器学习的许多新研究。我建议的研究将集中在两个非常一般的数据分析问题，我称之为横向和纵向信息选择这两类方法的发展。 假设我们有一个数据矩阵，其中行是观测值，列是变量。所谓“垂直”选择，我指的是重要行的选择。在应用程序中，这些行可以是“坏人”（例如，欺诈性金融交易）或“好人”（例如，具有抑制某种病毒的潜力的化学分子）。所谓“横向”选择，我指的是重要栏目的选择。在应用程序中，这些列是影响感兴趣结果的最重要因素（例如，用于检测某种疾病的生物标志物，或与某种癌症相关的基因）。由于这些选择问题的普遍性，我提出的研究预计将具有广泛的实际影响。例如，构建用于垂直选择的非常有效的核机器的一般范例可以使广泛的应用研究受益，从欺诈检测到药物发现。 我将研究我提出的新想法，如“深度内核机器”（DKM）和“变量选择集成”（VSE），以及建立分析和信息学实验室（AI Lab; http：//www.math.uwaterloo.ca/tumailab）。对DKM和VSE的深入理解将进一步推动统计机器学习领域的发展。人工智能实验室将为学生提供一个平台，通过在互联网上创建数据驱动的应用程序（如推荐系统），接受机器学习和数据挖掘方面的培训和研究。这些制度与我的建议的主题密切相关。他们分析数据矩阵，其列是项目，其行是用户，目标是向每个用户推荐几个项目，或者共同解决每个固定垂直维度的水平选择问题。