CIF: Small: Weakly Supervised Learning

传统的模式识别方法需要访问标记的训练数据，这些数据由所考虑的每个类的已知实例组成。这种方法被称为“监督”，因为训练标签被假设为正确的。然而，在许多模式识别应用中，很难或不可能获得精确的标签信息。本研究考察了涉及污染训练数据的分类任务，其中一些或所有感兴趣的类的训练示例被其他类的示例污染。应用包括文件分类、核不扩散、网络入侵检测、药物设计以及图像和视频注释。当在这些设置中应用标准分类算法时，产生次优分类器。不幸的是，目前还没有令人满意的理论或方法框架，同时解决这些分类问题的特点是污染的数据。为了解决这一缺点，本研究开发了一种新的框架，相互污染的概率分布的去污，以及相关的估计和分类方法。净化策略包括将观测到的分布投影到其他观测到的分布的凸船体上，并从该投影的残差中恢复感兴趣的真实分布。该投影是相对于被称为分离距离的统计距离，并且在关于污染量和底层类分布的纯度的充分条件下，该投影过程成功地恢复了类分布。这反过来又有利于最佳分类器的设计。该项目详细研究了噪声标签分类、异常检测、众包、半监督学习、领域自适应、迁移学习、多实例学习和部分标签学习等问题。

Traditional approaches to pattern recognition require access to labeled training data, consisting of known instances of each class under consideration. Such methods are known as "supervised" because the training labels are assumed to be correct. In many pattern recognition applications, however, precise label information is difficult or impossible to obtain. This research examines classification tasks involving contaminated training data, wherein training examples for some or all classes of interest are contaminated by examples of other classes. Applications include document classification, nuclear nonproliferation, network intrusion detection, drug design, and image and video annotation. When standard classification algorithms are applied in these settings, suboptimal classifiers result. Unfortunately, there currently exists no satisfactory theoretical or methodological framework that simultaneously addresses such classification problems characterized by contaminated data. To address this shortcoming, this research develops a novel framework for the decontamination of mutually contaminated probability distributions, together with associated estimation and classification methods. The decontamination strategy involves projecting observed distributions onto the convex hull of other observed distributions, and recovering the true distributions of interest from the residual of this projection. The projection is with respect to a statistical distance known as the separation distance, and under sufficient conditions on the amount of contamination and purity of the underlying class distributions, this projection procedure successfully recovers the class distributions. This in turn facilitates the design of optimal classifiers. The project examines in detail the problems of classification with noisy labels, anomaly detection, crowdsourcing, semi-supervised learning, domain adaptation, transfer learning, multiple instance learning, and learning from partial labels.