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• W2057017933 abstract "Linear Discriminant Analysis (LDA) and its nonlinear kernel variation Generalized Discriminant Analysis (GDA) are the most popular supervised dimensionality reduction methods for fault diagnosis. However, we argue that they probably provide suboptimal results for fault diagnosis due to the Fisher's criterion they use. This paper proposes a new supervised dimensionality reduction method named Locality Preserving Discriminant Analysis (LPDA) and its kernel variation Kernel LPDA (KLPDA) for fault diagnosis. (K)LPDA maximizes a new criterion such that local discriminant structure and local geometric structure in data are optimally preserved simultaneously in each dimension of the reduced space. The criterion directly targets at minimizing local overlapping between different classes. Extensive simulations on the Tennessee Eastman (TE) benchmark simulation process and a waste water treatment plant (WWTP) clearly demonstrate the superiority of our methods in terms of misclassification rate and making use of extra training data." @default.
• W2057017933 created "2016-06-24" @default.
• W2057017933 creator A5023198186 @default.
• W2057017933 creator A5042423456 @default.
• W2057017933 creator A5063760888 @default.
• W2057017933 date "2013-02-01" @default.
• W2057017933 modified "2023-09-25" @default.
• W2057017933 title "Fault diagnosis by Locality Preserving Discriminant Analysis and its kernel variation" @default.
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• W2057017933 doi "https://doi.org/10.1016/j.compchemeng.2012.10.008" @default.
• W2057017933 hasPublicationYear "2013" @default.
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