Unsupervised verification of laser-induced breakdown spectroscopy dataset clustering

详细信息    查看全文

• 作者：Michał R. Wó ; jcik ; ^{michal.wojcik@pwr.edu.pl} ; Rafał Zdunek ; Arkadiusz J. Antończak
• 关键词：Laser-induced breakdown spectroscopy ; Dataset clustering ; Unsupervised learning ; Linear discriminant analysis ; K-means
• 刊名：Spectrochimica Acta Part B: Atomic Spectroscopy
• 出版年：2016
• 出版时间：1 December 2016
• 年：2016
• 卷：126
• 期：Complete
• 页码：84-92
• 全文大小：4031 K
• 卷排序：126

文摘

An unsupervised method of LIBS dataset clustering was proposed. Prior to the calculation no information about the dataset has been provided. LDA has been used as a verification tool for the particular partitioning algorithm. Estimation of credible number of PCs in correspondence to their variance was proposed. The superiority of cosine distance measure for high dimensionality data was proven.