Unsupervised Deep Learning Framework for Solving One Class Classification Problems

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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