Spectral Similarity Measure Method Based on Neighborhood Counting
SONG Chun-jing1, DING Xiang-qian2, XU Peng-min3, LÜ Guang-jie3
1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2. Information Engineering Center, Ocean University of China, Qingdao 266100, China
3. Network Management Center, Qingdao Agricultural University, Qingdao 266109, China
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.
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