CPC G06F 16/26 (2019.01) [G06F 16/215 (2019.01); G06N 20/00 (2019.01)] | 18 Claims |
1. A computer-implemented quality determination method, the method comprising:
performing a dimensionality reduction on a high-dimensional dataset to form a dimensional-reduced dataset;
calculating relative proximities between a selected single data-point and a perturbed neighborhood both for the high-dimensional dataset and in an embedding; and
determining, using a machine-learning tool executed on a computing device, a quality of the dimensional-reduced dataset based on the relative proximities via a review of an extracted feature extracted from the dimensional-reduced dataset,
wherein the determining the quality further comprises:
identifying a K-ary neighborhood for the single data-point using an unsupervised nearest-neighbor search algorithm;
amplifying the K-ary neighborhood by approximating a locality of the point by sampling a finite number of points uniformly at a random interval around the single data-point; and
calculating feature distance contributions and feature influence explanations, the feature influence explanations being obtained as a linear correlation between the feature distance contributions and increasing proximity in the neighborhood.
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