CAREER: III: Modeling the Heterogeneity of Heterogeneity: Algorithms, Theories and Applications

许多高影响力的数据挖掘应用程序表现出多种类型异质性的共存，例如不同的分类任务、不同的数据源和不同的标签预言。它具有广泛的适用性，例如欺诈检测、制造、运输、医疗保健等。该项目旨在回答两个基本问题：（Q1）如何联合建模多种类型的异质性？ （Q2）如何从理论上表征模型泛化性能？预计它将推进仅限于单一类型异质性的最先进数据挖掘技术的算法和理论基础以及关于建模双异质性的稀疏文献。由此产生的算法和理论将被纳入新课程开发和多项 K-12 推广活动中。它可以使多种类型的异构性共存的各种实际应用受益。与工业合作伙伴的密切合作有望对包括安全和制造在内的两个应用领域产生及时且可衡量的影响。特别是，该项目致力于开发一个统一的总体数据挖掘框架，并具有三个互补的研究重点。第一个推动力创建了一套有效且高效的算法，用于对多种类型异质性的共存进行建模。关键思想是在参数空间上引入联合正则化器来模拟不同类型异质性之间的相互作用。第二个推力从理论上描述了模型的泛化性能，特别是它如何受到（1）多种类型异质性的共存以及（2）违反每种类型异质性背后的基本假设的影响。第三个主旨系统地评估了前两个主旨的算法和理论在实际应用中的情况。更多详细信息请访问：http://faculty.engineering.asu.edu/jingruihe/lab-2/projects/heterogeneous-learning/。

Many high-impact data mining applications exhibit the co-existence of multiple types of heterogeneity, such as different classification tasks, different data sources, and different labeling oracles. It has broad applicability such as fraud detection, manufacturing, transportation, healthcare, etc. This project aims to answer two fundamental questions: (Q1) how to jointly model multiple types of heterogeneity? (Q2) how to theoretically characterize the model generalization performance? It is expected to advance the algorithmic and theoretical foundations of state-of-the-art data mining techniques limited to a single type of heterogeneity and the sparse literature on modeling dual heterogeneity. The resulting algorithms and theories will be assimilated into new curriculum development and multiple K-12 outreach activities. It could benefit various real applications where multiple types of heterogeneity co-exist. A close collaboration with industrial partners promises timely and measurable impacts on two application domains, including security and manufacturing.In particular, this project strives to develop a unified, overarching data mining framework, with three complementary research thrusts. The first thrust creates a suite of effective and efficient algorithms for modeling the co-existence of multiple types of heterogeneity. The key idea is to introduce a joint regularizer on the parameter space to model the interplay among different types of heterogeneity. The second thrust theoretically characterizes the model generalization performance, especially how it is affected by (1) the co-existence of multiple types of heterogeneity and (2) the violations to the underlying assumptions behind each type of heterogeneity. The third thrust systematically evaluates the algorithms and theories from the first two thrusts on real applications. More details can be found at: http://faculty.engineering.asu.edu/jingruihe/lab-2/projects/heterogeneous-learning/.