Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification
Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substant...
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MDPI AG
2025-02-01
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| author | YoungJae Son Tiejun Chen Sung-June Baek |
| author_facet | YoungJae Son Tiejun Chen Sung-June Baek |
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| collection | DOAJ |
| description | Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. The proposed method employs principal component transformation (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>C</mi><mi>T</mi></mrow></semantics></math></inline-formula>) as its foundational framework, similar to existing techniques. A running average filter is applied to reduce noise in the input data, which reduces the number of principal components (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>C</mi><mi>s</mi></mrow></semantics></math></inline-formula>) necessary to represent the data. Subsequently, a <i>k</i>-<i>d</i> tree is employed to identify a relatively similar spectrum, which efficiently constrains the search space. Additionally, fine search strategies leveraging precomputed distances enhance the existing pilot search method by dynamically updating candidate spectra, thereby improving search efficiency. Experimental results demonstrate that the proposed method achieves accuracy comparable to exhaustive search methods while significantly reducing computational complexity relative to existing approaches. |
| format | Article |
| id | doaj-art-5ccdcc2579e942e58df80d11e55bf595 |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-5ccdcc2579e942e58df80d11e55bf5952025-08-20T02:44:53ZengMDPI AGMathematics2227-73902025-02-0113457410.3390/math13040574Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data IdentificationYoungJae Son0Tiejun Chen1Sung-June Baek2Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Republic of KoreaDepartment of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju 61186, Republic of KoreaSpectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. The proposed method employs principal component transformation (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>C</mi><mi>T</mi></mrow></semantics></math></inline-formula>) as its foundational framework, similar to existing techniques. A running average filter is applied to reduce noise in the input data, which reduces the number of principal components (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>P</mi><mi>C</mi><mi>s</mi></mrow></semantics></math></inline-formula>) necessary to represent the data. Subsequently, a <i>k</i>-<i>d</i> tree is employed to identify a relatively similar spectrum, which efficiently constrains the search space. Additionally, fine search strategies leveraging precomputed distances enhance the existing pilot search method by dynamically updating candidate spectra, thereby improving search efficiency. Experimental results demonstrate that the proposed method achieves accuracy comparable to exhaustive search methods while significantly reducing computational complexity relative to existing approaches.https://www.mdpi.com/2227-7390/13/4/574fast searchspectroscopy identification<i>k</i>-<i>d</i> tree searchfine search |
| spellingShingle | YoungJae Son Tiejun Chen Sung-June Baek Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification Mathematics fast search spectroscopy identification <i>k</i>-<i>d</i> tree search fine search |
| title | Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification |
| title_full | Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification |
| title_fullStr | Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification |
| title_full_unstemmed | Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification |
| title_short | Fast Search Using <i>k</i>-<i>d</i> Trees with Fine Search for Spectral Data Identification |
| title_sort | fast search using i k i i d i trees with fine search for spectral data identification |
| topic | fast search spectroscopy identification <i>k</i>-<i>d</i> tree search fine search |
| url | https://www.mdpi.com/2227-7390/13/4/574 |
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