Visualizing and Classifying Odors Using a Similarity Matrix

Liran Carmel; Yehuda Koren; D. Harel

Visualizing and Classifying Odors Using a Similarity Matrix

Liran Carmel, Yehuda Koren, D. Harel

Department of Genetics

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

Abstract

The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normalized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of the data, and classification of new samples. The visualization is carried out using graph drawing tools, and the results are shown to bear some desired properties. The classification is done using a majority-decision type algorithm, and is demonstrated to have very low error rate.

Original language	American English
Title of host publication	Proceedings of the 9th International Symposium on Olfaction and Electronic Nose ISOEN '02
Place of Publication	Rome
Publisher	Aracne Editrice
Pages	141-146
Number of pages	6
State	Published - 2003

Keywords

electronic noses
similarity index
feature extraction
Lorentzian model
graph drawing
Visualization
classification

Cite this

@inproceedings{e392515ae7fc421abbf340ad680d6b6a,

title = "Visualizing and Classifying Odors Using a Similarity Matrix",

abstract = "The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normalized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of the data, and classification of new samples. The visualization is carried out using graph drawing tools, and the results are shown to bear some desired properties. The classification is done using a majority-decision type algorithm, and is demonstrated to have very low error rate.",

keywords = "electronic noses, similarity index, feature extraction, Lorentzian model, graph drawing, Visualization, classification",

author = "Liran Carmel and Yehuda Koren and D. Harel",

year = "2003",

language = "American English",

pages = "141--146",

booktitle = "Proceedings of the 9th International Symposium on Olfaction and Electronic Nose ISOEN '02",

publisher = "Aracne Editrice",

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T1 - Visualizing and Classifying Odors Using a Similarity Matrix

AU - Carmel, Liran

AU - Koren, Yehuda

AU - Harel, D.

PY - 2003

Y1 - 2003

N2 - The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normalized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of the data, and classification of new samples. The visualization is carried out using graph drawing tools, and the results are shown to bear some desired properties. The classification is done using a majority-decision type algorithm, and is demonstrated to have very low error rate.

AB - The Lorentzian model is an analytic expression that describes the time response of electronic nose sensors. We show how this model can be utilized to calculate a normalized similarity index between any two measurements. The set of similarity indices is then used for two purposes: visualization of the data, and classification of new samples. The visualization is carried out using graph drawing tools, and the results are shown to bear some desired properties. The classification is done using a majority-decision type algorithm, and is demonstrated to have very low error rate.

KW - electronic noses

KW - similarity index

KW - feature extraction

KW - Lorentzian model

KW - graph drawing

KW - Visualization

KW - classification

M3 - Conference contribution

SP - 141

EP - 146

BT - Proceedings of the 9th International Symposium on Olfaction and Electronic Nose ISOEN '02

PB - Aracne Editrice

CY - Rome

ER -

Visualizing and Classifying Odors Using a Similarity Matrix

Abstract

Keywords

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