A variety of statistical approaches for novelty and outlier estimation methods exist within the literature . While these offer convenient statistical interpretations, they are found to rely on user selected parameters that drastically influence novelty classification outcomes. Additionally, they often make distribution assumptions that are not guaranteed in a materials datasets and may not reflect the local structure of the data. The recent AUTOGLOSH approach attempts to remedy this by providing a data-driven method for selecting optimal parameters. However, this method was found to perform poorly when sharp distinctions between points aren't observed within the data.