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Linear discriminant analysis

About: Linear discriminant analysis is a research topic. Over the lifetime, 18361 publications have been published within this topic receiving 603195 citations. The topic is also known as: Linear discriminant analysis & LDA.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a support vector machine (SVM) technique was used for finger-vein pattern identification in a personal identification system using an adaptive neuro-fuzzy inference system (ANFIS).
Abstract: This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light The proposed verification system consists of image pre-processing and pattern classification In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS) The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA The features are then used in pattern classification and identification The accuracy of classification using SVM is 98% and only takes 0015 s The result shows a superior performance to the artificial neural network of ANFIS in the proposed system

121 citations

Journal ArticleDOI
TL;DR: It is shown that by combining classifier ensemble algorithms in this two-step manner, it is possible to predict the malignancy for solitary pulmonary nodules with a performance exceeding that of either of the individual steps.

121 citations

Journal ArticleDOI
TL;DR: The weighted-Parzen-window classifier requires less computation and storage than the full Parzen- window classifier, and Experimental results showed that significant savings could be achieved with only minimal, if any, error rate degradation for synthetic and real data sets.
Abstract: This paper introduces the weighted-Parzen-window classifier. The proposed technique uses a clustering procedure to find a set of reference vectors and weights which are used to approximate the Parzen-window (kernel-estimator) classifier. The weighted-Parzen-window classifier requires less computation and storage than the full Parzen-window classifier. Experimental results showed that significant savings could be achieved with only minimal, if any, error rate degradation for synthetic and real data sets.

121 citations

Journal ArticleDOI
TL;DR: The statistical results demonstrate that among applied methods, random forest and quadratic discriminant analysis are, respectively, preferable with the imbalanced and balanced datasets since they show the highest efficiency in predicting the structural responses.

121 citations

Journal ArticleDOI
TL;DR: It is concluded that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes, but their performance improves markedly with increasing sample size up to a point at which they outperform the other methods.

121 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20251
20242
2023756
20221,711
2021678
2020815