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. 2022 Sep 15;12:15504. doi: 10.1038/s41598-022-19771-8

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© The Author(s) 2022

Open Access This 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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Two-dimensional feature subspace for algorithms on dataset C3 under four noise levels. For comparing the feature extraction capability of each algorithm, the data points were colored according to the ground truth. Red, green and blue dots represent the features extracted from three clusters, respectively. The last column shows the average spike waveforms of three clusters obtained by LDA-DP, and the shaded part represents the standard deviation.