拉曼微光谱数据信息特征选择方法

A. Karmenyan, D. Vrazhnov, E. Sandykova, E. Perevedentseva, A. Krivokharchenko, V. Nadtochenko, Chia-Liang Cheng, T. Kabanova, Tatyana E. Malakhova
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引用次数: 0

摘要

提出了一种基于低阶统计量的拉曼光谱信息特征提取算法。该方法在小鼠着床前胚胎上进行了拉曼光谱实验。应用监督和无监督机器学习方法来选择最具信息量的特征来测试处理数据的可分离性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Informative feature selection method for Raman micro-spectroscopy data
The paper presents an algorithm based on low order statistics for the informative feature extraction for Raman spectroscopy data. The proposed method was tested on mouse preimplantation embryos Raman spectra. Both supervised and unsupervised machine learning methods were applied to selected the most informative features to test the separability of the processed data.
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