title: A new kernel discriminant analysis framework for electronic nose recognition.
abstract: Electronic nose (e-Nose) technology based on metal oxide semiconductor gas sensor array is widely studied for detection of gas components. This paper proposes a new discriminant analysis framework (NDA) for dimension reduction and e-Nose recognition. In a NDA, the between-class and the within-class Laplacian scatter matrix are designed from sample to sample, respectively, to characterize the between-class separability and the within-class compactness by seeking for discriminant matrix to simultaneously maximize the between-class Laplacian scatter and minimize the within-class Laplacian scatter. In terms of the linear separability in high dimensional kernel mapping space and the dimension reduction of principal component analysis (PCA), an effective kernel PCA plus NDA method (KNDA) is proposed for rapid detection of gas mixture components by an e-Nose. The NDA framework is derived in this paper as well as the specific implementations of the proposed KNDA method in training and recognition process. The KNDA is examined on the e-Nose datasets of six kinds of gas components, and compared with state of the art e-Nose classification methods. Experimental results demonstrate that the proposed KNDA method shows the best performance with average recognition rate and total recognition rate as 94.14% and 95.06% which leads to a promising feature extraction and multi-class recognition in e-Nose.
authors.affiliation: College of Communication Engineering, Chongqing University, 174 ShaZheng street, ShaPingBa District, Chongqing 400044, China; Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. Electronic address: leizhang@cqu.edu.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: College of Communication Engineering, Chongqing University, 174 ShaZheng street, ShaPingBa District, Chongqing 400044, China.
authors.forename: Feng-Chun
authors.initials: FC
authors.lastname: Tian
mesh_terms: D016002:Discriminant Analysis; D062609:Electronic Nose; D005740:Gases / Q000032:analysis; D025341:Principal Component Analysis
chemical_list: D005740:Gases
keywords: Dimension reduction; Discriminant analysis; Electronic nose; Feature extraction; Multi-class recognition