基于邻近集计算的光谱相似性测度方法研究

doi:10.3964/j.issn.1000-0593(2017)07-2032-04

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Abstract In terms of the near infrared spectrum data similarity measurement,due to the fact that spectral data is high dimensional, non-linear and overlapping, data processing is full of difficulties failures to measure distance. The traditional method of similarity measurement in high dimensional space presented unadapted,so this paper proposed the spectral similarity measure method based on neighborhood counting. Firstly, using the (NPP) algorithm to handle the original spectral data, as a dimension reduction method, can preserve the original nonlinear spectral data structure and neighborhood information.Then, in the low dimensional space, improved neighborhood counting method is used to realize the similarity measure of the near infrared spectrum data. Experimental results show that the spectral similarity measuring method based on neighborhood counting is effective in the spectral data similarity measurement, which has a good prospect in tobacco style determination and quality analysis. Besides, it provides a good similarity measurement solution in high dimensional spectral data.

Key words： Near infrared spectra Neighborhood preserving projections Neighborhood counting Similarity measure

Received: 2015-06-25 Accepted: 2015-12-09

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O657.3

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2017)07-2032-04 OR http://www.gpxygpfx.com/EN/Y2017/V37/I07/2032