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. 2021 Feb 19;11:4247. doi: 10.1038/s41598-021-83269-y

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Classification and feature extraction benchmarks. The top two d-modes project the raw data into two dimensions for the (a) Iris and (b) wine datasets. The mean and STD of data packets are shown within a mode feature space plane for the top d-mode that describes the (c) Iris and (d) wine datasets. In the right panel, four tabulated graphs show egg reconstruction percentages in the discriminant (correct) and indifferent (incorrect) subspaces as a function of system size for 4, 20 and 100 observations per variable (OPV). System size is characterized by the number of df. (e–h) Four tabulated plots are shown for a (large, small) egg placed in (SGN, CGN). Egg reconstruction percentages are shown for discriminant (correct) and indifferent (incorrect) subspaces as a function of df at an OPV of 4, 20 and 100.