Natural Outlier Rejection with Shepherd's Psychometric Similarity Metric

Abstract

The human mind does not recognize absolute distances. Instead, it seeks comparisons based on similarity, often called psychometric metrics. While many psychometric metrics have been used in cognitive studies, they are seldom used for machine learning or neural computing studies. In this paper, we present a case of Shepherd's similarity metric that can be effective in naturally removing outliers in natural language classification problems. Natural language processing uses multiple cognitive regions of the human brain, investigations of which can help with developmental studies of the human mind. The proposed similarity metric can help understand the causal links of language processing, giving a sense of human mind functions. A comparison with other similarity metrics indicates that Shepherd's similarity shows unusual tolerance to noise changes and the ability to reject outliers naturally.