Outlier Detection Using Immersive Analytics With Virtual Reality

ABSTRACT This paper honors Paul Geladi with a new chemometric data visualization approach based on immersive analytics principles using virtual reality (VR). With the many technological advancements improving the accessibility of extended reality (XR), including VR, the frontier of immersive analytics is wide open for utilizing VR as a powerful chemometric data analysis tool. Immersion of human senses into a virtually generated three‐dimensional (3D) world compels us to act more instinctively in complex data decision‐making scenarios by using our inherent cognitive pattern recognition capacity learned over years of experience. Proposed is an immersive analytics application of VR for a hybrid human/computer‐aided outlier detection process. In this application, each training set sample is virtually realized as a glyph to visually assess the inter‐ and intra‐sample relationships present in datasets while mining for nonrepresentative samples. Sample glyph shape and size are developed using hundreds of sample similarity measurements for each sample based both on spectral and prediction property information. These sample glyphs are visually and spatially compared with one another in VR by the user. Outlier checking in VR safeguards against masking and swamping problems that are difficult to recognize with automatic algorithms. Results from dataset situations based on near infrared (NIR) and ultraviolet (UV) spectra and analyte reference values show the viability of using VR for data analysis and outlier detection. This VR application demonstrates the looming evolution of immersive analytics with an XR interface involving human reasoning in difficult chemometric data analysis settings.