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，在训练阶段只有来自正/正常类的样本可用，而负样本要么完全不存在，要么采样不良，要么以未标记数据的形式存在。在正常数据容易收集、标记或大量可用的情况下，OCC的重要性变得迫在眉睫，而负面数据要么不可用，要么很少发生，难以标记，获取成本太高，要么收集可能导致健康和安全危害。OCC范式在各种领域中找到应用，包括机器故障诊断、异常健康模式、欺诈检测、视频中的暴力检测、异常环境事件、罕见疾病检测、音频监控中的不可见行为以及癌细胞检测等。在这些应用程序中，负数据可能在训练阶段不可用，但它可以在测试阶段发生。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OCC方法的一个主要问题是，它们需要对原始数据执行特定于域的特征提取，这不仅是自组织的，而且是一个繁琐、耗时且容易出错的过程。在没有负面数据的情况下，OCC中的特征提取变得更具挑战性。目前存在几种基于神经网络的解决方案来处理OCC问题;然而，显然缺乏解决这些问题的深度学习框架。因此，我认为OCC范式研究中最有前途的未来方向主要在于开发创新的无监督深度学习解决方案，以促进自动特征学习。在这个发现补助金中，我的长期目标是开发一个研究计划，其中包括开发一个用于解决OCC问题的无监督深度学习框架。为了成功地实现这一目标，以下是我的具体目标： 1.开发新颖的无监督深度学习算法及其集成，以提供性能增强。 2.处理异构数据并开发自适应多模态无监督深度学习方法，以提供强大的性能。 3.使用深度学习方法为正向无标签学习问题创建新的解决方案。 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。这个发现研究补助金是我在这个领域的研究工作的自然延续，这是通过我的出版记录证明。这笔赠款在以下方面具有很大的影响： 开发新型无监督深度学习算法和网络架构， 解决可能对健康、安全、经济和环境产生不利影响的真实的世界问题， 通过共享数据集、代码和算法传播知识， 培养六名高素质的HQP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.