Haussdorff and hellinger for colorimetric sensor array classification

Tommy Sonne Alstrøm; Bjørn Sand Jensen; Mikkel Nørgaard Schmidt; Natalie Kostesha; Jan Larsen

doi:10.1109/MLSP.2012.6349724

Haussdorff and hellinger for colorimetric sensor array classification

Tommy Sonne Alstrøm, Bjørn Sand Jensen, Mikkel Nørgaard Schmidt, Natalie Kostesha, Jan Larsen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

Development of sensors and systems for detection of chemical compounds is an important challenge with applications in areas such as anti-terrorism, demining, and environmental monitoring. A newly developed colorimetric sensor array is able to detect explosives and volatile organic compounds; however, each sensor reading consists of hundreds of pixel values, and methods for combining these readings from multiple sensors must be developed to make a classification system. In this work we examine two distance based classification methods, K-Nearest Neighbor (KNN) and Gaussian process (GP) classification, which both rely on a suitable distance metric. We evaluate a range of different distance measures and propose a method for sensor fusion in the GP classifier. Our results indicate that the best choice of distance measure depends on the sensor and the chemical of interest.

Original language	English
Title of host publication	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Number of pages	6
Place of Publication	978-1-4673-1025-3
Publisher	IEEE
Publication date	2012
ISBN (Print)	978-1-4673-1024-6
DOIs	https://doi.org/10.1109/MLSP.2012.6349724
Publication status	Published - 2012
Event	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Santander, Spain Duration: 23 Oct 2012 → 26 Oct 2012 http://mlsp2012.conwiz.dk/

Conference

Conference	2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Country	Spain
City	Santander
Period	23/10/2012 → 26/10/2012
Internet address	http://mlsp2012.conwiz.dk/

Series	Machine Learning for Signal Processing
ISSN	1551-2541

Keywords

Hausdorff distance
Hellinger distance
Chemo–selective compounds
Feature extraction
K–nearest neighbor classification
Gaussian Process Classification

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

Access to Document

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title = "Haussdorff and hellinger for colorimetric sensor array classification",

abstract = "Development of sensors and systems for detection of chemical compounds is an important challenge with applications in areas such as anti-terrorism, demining, and environmental monitoring. A newly developed colorimetric sensor array is able to detect explosives and volatile organic compounds; however, each sensor reading consists of hundreds of pixel values, and methods for combining these readings from multiple sensors must be developed to make a classification system. In this work we examine two distance based classification methods, K-Nearest Neighbor (KNN) and Gaussian process (GP) classification, which both rely on a suitable distance metric. We evaluate a range of different distance measures and propose a method for sensor fusion in the GP classifier. Our results indicate that the best choice of distance measure depends on the sensor and the chemical of interest.",

keywords = "Hausdorff distance, Hellinger distance, Chemo–selective compounds, Feature extraction, K–nearest neighbor classification, Gaussian Process Classification",

author = "Alstr{\o}m, {Tommy Sonne} and Jensen, {Bj{\o}rn Sand} and Schmidt, {Mikkel N{\o}rgaard} and Natalie Kostesha and Jan Larsen",

year = "2012",

doi = "10.1109/MLSP.2012.6349724",

language = "English",

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series = "Machine Learning for Signal Processing",

booktitle = "2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)",

publisher = "IEEE",

address = "United States",

note = "2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) ; Conference date: 23-10-2012 Through 26-10-2012",

url = "http://mlsp2012.conwiz.dk/",

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Alstrøm, TS, Jensen, BS, Schmidt, MN, Kostesha, N & Larsen, J 2012, Haussdorff and hellinger for colorimetric sensor array classification. in 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 978-1-4673-1025-3, Machine Learning for Signal Processing, 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Santander, Spain, 23/10/2012. https://doi.org/10.1109/MLSP.2012.6349724

Haussdorff and hellinger for colorimetric sensor array classification. / Alstrøm, Tommy Sonne; Jensen, Bjørn Sand; Schmidt, Mikkel Nørgaard; Kostesha, Natalie; Larsen, Jan.

2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). 978-1-4673-1025-3 : IEEE, 2012. (Machine Learning for Signal Processing).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Haussdorff and hellinger for colorimetric sensor array classification

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AU - Jensen, Bjørn Sand

AU - Schmidt, Mikkel Nørgaard

AU - Kostesha, Natalie

AU - Larsen, Jan

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AB - Development of sensors and systems for detection of chemical compounds is an important challenge with applications in areas such as anti-terrorism, demining, and environmental monitoring. A newly developed colorimetric sensor array is able to detect explosives and volatile organic compounds; however, each sensor reading consists of hundreds of pixel values, and methods for combining these readings from multiple sensors must be developed to make a classification system. In this work we examine two distance based classification methods, K-Nearest Neighbor (KNN) and Gaussian process (GP) classification, which both rely on a suitable distance metric. We evaluate a range of different distance measures and propose a method for sensor fusion in the GP classifier. Our results indicate that the best choice of distance measure depends on the sensor and the chemical of interest.

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KW - Hellinger distance

KW - Chemo–selective compounds

KW - Feature extraction

KW - K–nearest neighbor classification

KW - Gaussian Process Classification

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DO - 10.1109/MLSP.2012.6349724

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T3 - Machine Learning for Signal Processing

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