One-Class Classification with Hyperdimensional Computing
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One-Class Classification with Hyperdimensional Computing

  • Author(s): Watkinson Medina, Neftali David
  • Advisor(s): Nicolau, Alexandru
  • et al.
Abstract

Contemporary research in cognitive and neurological sciences confirms that human brains perform object detection and classification by identifying membership to a single class. When observing a scene with various objects, we can quickly point out and answer queries about the object we recognize, without needing to know what the unknown objects are. Within the field of machine learning (ML), the closest algorithm that emulates this behavior is one-class classification (OCC). With this approach, models are trained using samples of a single class in order to identify membership or anomalies from query instances. However, research about OCC is scarce and most approaches focus on repurposing models that were designed for binary or multi-class classification, resulting in suboptimal performance. A novel, neuro-inspired approach to computing, called Hyperdimensional (HD) computing, promises to be closer than traditional approaches to how humans encode information. With HD computing we have the opportunity to design OCC models without having to manipulate multi-class models. This makes for a more straightforward approach that can be easily tuned to the problem requirements. In this dissertation I present Hyperdimensional One-class classification (HD-OCC). The modeling approach uses the power of HD computing to identify anomalies among sampled data. Also, I discuss how hyperdimensional encoding works for OCC. The encoding process is similar to those used in multi-class classification and can be reused across models. HD-OCC is tested using three different use case scenarios. The first focuses on predicting future diagnosis of type 2 diabetes among members of the Pima Indian community. This experiment illustrates the impact of linear encoding within HD-OCC and provides a baseline comparison against ML algorithms. The second experiment uses patient data to model sepsis and predict septic shock in patients within the intensive care unit. This real-case scenario adds a different challenge in introducing sequential features to the dataset. Finally, HD-OCC is applied towards image processing by using pulmonary CT scans to detect patients with anomalies, including detecting patients with a COVID-19 infection. The results show that HD-OCC performs well and that it is versatile enough to be applied to different types of input. Also, that HD computing is a promising framework to drive research towards true artificial intelligence.

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