Lossless Interpretable Glyphs for Visual Knowledge Discovery in High-Dimensional Data
Nicholas Cutlip, Boris Kovalerchuk · 2023
The expansion of the use of a machine learning (ML) technology in multiple domains heavily depends on acceptance of these models by end users and their abilities to understand the patterns discovered by machine learning algorithms. The simplicity and understandability of those patterns by the domain expert is critical for success of such ML explorations. This paper proposes a method for designing lossless compact and interpretable machine learning visual patterns to be easily captured and remembered by the domain experts due to their compactness and simplicity. This glyph approach is illustrated with a “bird” glyph. This glyph is a combination of Stick Figures and a type of General Line Coordinates known as Shifted Paired Coordinates. The efficiency of the approach is demonstrated on several benchmark machine learning data sets.