Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Jun-Kyu Park, Suwoong Lee, Aaron Park and Sung-June Baek
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Jun-Kyu Park: Safety System R&D Group, Korea Institute of Industrial Technology, Dague 31056, Korea
Suwoong Lee: Safety System R&D Group, Korea Institute of Industrial Technology, Dague 31056, Korea
Aaron Park: Department of Electronics Engineering, Chonnam National University, Gwangju 61186, Korea
Sung-June Baek: Department of Electronics Engineering, Chonnam National University, Gwangju 61186, Korea

Mathematics, 2020, vol. 8, issue 11, 1-14

Abstract: In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

Keywords: fast search; vector quantization; cluster search; pilot search; Raman spectroscopy identification (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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