Unsupervised Deep Learning Framework for Solving One Class Classification Problems

单类分类(OCC)是一种特殊的机器学习范式，在训练阶段只有正/正常类的样本可用，而负的样本要么完全不存在，要么样本质量差，要么以未标记数据的形式存在。在以下情况下，控制中心的重要性变得迫在眉睫：正常数据很容易收集、标记或大量可用，而负面数据要么不可用，要么很少发生，很难标记，获取成本太高，或者其收集可能导致健康和安全危害。OCC范式在各种领域都有应用，包括机器故障诊断、异常健康模式、欺诈检测、视频暴力检测、异常环境事件、罕见疾病检测、音频监控中的看不见的行为，以及癌细胞检测等。在这些应用中，负数据可能在训练阶段不可用，但可能在测试阶段出现。检测其发生是非常重要的，因为这会对健康、安全、经济和环境产生不利影响。 传统的OCC方法的一个主要问题是需要对原始数据进行特定领域的特征提取，这不仅是一个特别的过程，而且是一个繁琐、耗时和容易出错的过程。在没有负数据的情况下，特征提取在OCC中变得更加困难。有几种基于神经网络的解决方案可以处理OCC问题；然而，显然缺乏解决这些问题的深度学习框架。因此，我设想OCC范式研究中最有前途的未来方向在很大程度上取决于开发有助于自动特征学习的创新的无监督深度学习解决方案。在这笔发现基金中，我的长期目标是开发一个研究计划，其中包括开发一个解决OCC问题的无人监督的深度学习框架。为了成功实现这一目标，我的具体目标如下： 1.开发新的无监督深度学习算法及其集成以提高性能。 2.处理异质数据，开发自适应多模式无监督深度学习方法，以提供稳健的性能。 3.使用深度学习方法为正无标签学习问题创建新的解。 在我的研究生学习和博士后研究期间，我在OCC领域进行了广泛的工作。这项发现研究拨款是我在这一领域研究工作的自然延续，这从我的出版记录中得到了证明。这笔赠款在以下方面具有很高的影响 开发新的无监督深度学习算法和网络结构， 解决可能对健康、安全、经济和环境产生负面影响的现实世界问题， 通过共享数据集、代码和算法传播知识，以及 培养六名高素质的总部人员，成为该领域未来的领导者。

One-class classification (OCC) is a special machine learning paradigm where only the samples from the positive/normal class are available during the training phase, while the negative samples are either completely absent, poorly sampled or present in the form of unlabeled data. The importance of OCC becomes imminent in situations where the normal data is easy to collect, label or is available in abundance, whereas the negative data is either unavailable, occurs rarely, hard to label, too costly to obtain or its collection may cause health and safety hazards. The OCC paradigm finds applications in various fields, including machine fault diagnosis, abnormal health patterns, fraud detection, violence detection in videos, unusual environment events , rare disease detection, unseen behaviours in audio monitoring, and cancer cell detection, to name a few. In these applications, the negative data may not be available during training phase, yet it can occur during the testing phase. It is very important to detect its occurrence because that can adversely impact the health, safety, economics, and environment. A major problem in traditional OCC methods is that they require domain specific feature extraction to be performed on the raw data, which is not only ad-hoc but a tedious, time-consuming, and error-prone process. Feature extraction becomes even more challenging in OCC in the absence of negative data. Several neural networks based solutions exist to handle OCC problems; however, there is a clear absence of a deep learning framework for solving these problems. Therefore, I envisage that the most promising future direction in the research of OCC paradigm rests largely with developing innovative unsupervised deep learning solutions that facilitates automatic feature learning. In this discovery grant, my long term goal is to develop a research program that involves the development of an unsupervised deep learning framework for solving OCC problems. To achieve this goal successfully, following are my specific objectives: 1. Develop novel unsupervised deep learning algorithms and their ensemble to provide performance enhancements. 2. Handle heterogeneous data and develop adaptive multi-modal unsupervised deep learning methods to provide robust performance. 3. Create new solutions for positive-unlabeled learning problem using deep learning methods. During my graduate studies and postdoctoral research, I extensively worked in the field of OCC. This discovery research grant is a natural continuation of my research work in this field, which is demonstrated through my publication record. This grant has a high impact in terms of Developing novel unsupervised deep learning algorithms and network architectures, Addressing real world problems that can adversely impact the health, safety, economics, and environment, Disseminating knowledge by sharing datasets, code and algorithms, and Training six high quality HQPs to become future leaders of the field.