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An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

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An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification

Increasing the number of sensors in a gas identification system generally improves its performance as this will add extra features for analysis. However, this affects the computational complexity, especially if the identification algorithm is to be implemented on a hardware platform. Therefore, feature reduction is required to extract the most important information from the sensors for processing. In this paper, linear discriminant analysis (LDA) and principal component analysis (PCA)-based feature reduction algorithms have been analyzed using the data obtained from two different types of gas sensors, i.e., seven commercial Figaro sensors and in-house fabricated 4 4 tin-oxide gas array sensor.



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  • Model: PROJ7054
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This product was added to our catalog on Wednesday 31 May, 2017.

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