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Complex Heterogeneity Learning: A Theoretical and Empirical Study
Pattern Recognition ( IF 8 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.patcog.2020.107519
Pei Yang , Qi Tan , Jingrui He

Abstract Data heterogeneity such as task heterogeneity, view heterogeneity, and instance heterogeneity often co-exist in many real-world applications including insider threat detection, traffic prediction, brain image analysis, quality control in manufacturing processes, etc. However, most of the existing techniques might not take fully advantage of the rich heterogeneity. To address this problem, we propose a novel graph-based approach named M3 to simultaneously model triple heterogeneity in a principled framework. The main idea is to employ the hybrid graphs to jointly model the task relatedness, view consistency, and bag-instance correlation by enhancing the labeling consistency between nearby nodes on the graphs. Furthermore, we analyze the generalization performance of the proposed method based on Rademacher complexity, which sheds light on the benefits of jointly modeling multiple types of heterogeneity. The resulting optimization problem is challenging since the objective function is non-smooth and non-convex. We propose an iterative algorithm based on block coordinate descent and bundle method to solve the problem. Experimental results on various datasets demonstrate the effectiveness of the proposed method.

中文翻译:

复杂异构学习:理论和实证研究

摘要 任务异质性、视图异质性和实例异质性等数据异质性在许多实际应用中经常共存,包括内部威胁检测、流量预测、大脑图像分析、制造过程中的质量控制等。技术可能无法充分利用丰富的异质性。为了解决这个问题,我们提出了一种名为 M3 的基于图的新方法,以在原则框架中同时对三重异质性进行建模。主要思想是利用混合图通过增强图上相邻节点之间的标签一致性来联合建模任务相关性、视图一致性和袋实例相关性。此外,我们基于 Rademacher 复杂度分析了所提出方法的泛化性能,这阐明了联合建模多种类型的异质性的好处。由于目标函数是非光滑和非凸的,因此由此产生的优化问题具有挑战性。我们提出了一种基于块坐标下降和捆绑方法的迭代算法来解决这个问题。在各种数据集上的实验结果证明了所提出方法的有效性。
更新日期:2020-11-01
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