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的异构性共存，例如不同的分类任务，不同的数据源和不同的标记预言机。它具有广泛的适用性，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)如何从理论上描述模型的泛化性能？预计将推进国家的最先进的数据挖掘技术的算法和理论基础限于一个单一类型的异质性和稀疏的文献建模双异质性。由此产生的算法和理论将被吸收到新的课程开发和多个K-12推广活动。它可以使多种类型的异质性共存的各种真实的应用受益。与工业合作伙伴的密切合作有望对两个应用领域产生及时和可衡量的影响，包括安全和制造。特别是，该项目致力于开发一个统一的，总体的数据挖掘框架，具有三个互补的研究重点。第一个推力创建了一套有效和高效的算法，用于建模多种类型的异质性共存。其核心思想是在参数空间上引入一个联合正则化子来模拟不同类型异质性之间的相互作用。第二个重点从理论上描述了模型泛化性能的特征，特别是它如何受到（1）多种类型异质性的共存和（2）违反每种异质性背后的基本假设的影响。第三个推力系统地评估算法和理论，从真实的应用程序的前两个推力。更多详情请访问：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/.