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

Akbar, Muhammad Ali, Ait Si ali, Amine, Amira, Abbes, Bensaali, Faycal, Benammar, Mohieddine, Hassan, Muhammad and Bermak, Amine (2016) An Empirical Study for PCA- and LDA-Based Feature Reduction for Gas Identification. IEEE Sensors Journal, 16 (14). pp. 5734-5746. ISSN 1530-437X

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Official URL: https://doi.org/10.1109/JSEN.2016.2565721

Abstract

Abstract:
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. A decision tree-based classifier is used to examine the performance of both the PCA and LDA approaches. The software implementation is carried out in MATLAB and the hardware implementation is performed using the Zynq system-on-chip (SoC) platform. It has been found that with the 4×4 array sensor, two discriminant functions (DF) of LDA provide 3.3% better classification than five PCA components, while for the seven Figaro sensors, two principal components and one DF show the same performances. The hardware implementation results on the programmable logic of the Zynq SoC shows that LDA outperforms PCA by using 50% less resources as well as by being 11% faster with a maximum running frequency of 122 MHz.

Item Type: Article
Uncontrolled Keywords: Feature reduction, Gas identification, PCA, LDA, Electronic nose, Zynq SoC
Subjects: G400 Computer Science
G700 Artificial Intelligence
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Amine Ait Si ali
Date Deposited: 22 Aug 2017 09:35
Last Modified: 06 Sep 2017 21:26
URI: http://nrl.northumbria.ac.uk/id/eprint/31467

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