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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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Book ChapterDOI
01 Oct 2010
TL;DR: The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states.
Abstract: The ability to recognize emotion is one of the hallmarks of emotion intelligence This paper proposed to recognize emotion using physiological signals obtained from multiple subjects IAPS (International Affective Picture System) images were used to elicit target emotions Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract 30 features for recognition Two pattern classification methods, Fisher discriminant and SVM method are used and compared for emotional state classification The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states

106 citations

Journal ArticleDOI
TL;DR: It is shown that 2D-LDA has eliminated the information contained in the covariance information between different local geometric structures, which is useful for discriminant feature extraction, whereas 1D- LDA could preserve such information, and this new finding indicates that 1D -LDA is able to gain higher Fisher score than 2D/LDA in some extreme case.

106 citations

Journal ArticleDOI
TL;DR: High‐dimensional data often contain many variables that are irrelevant for predicting a response or for an accurate group assignment, which can lead to a loss in performance if the contribution of the variables to the model is small.
Abstract: High-dimensional data often contain many variables that are irrelevant for predicting a response or for an accurate group assignment. The inclusion of such variables in a regression or classification model leads to a loss in performance, even if the contribution of the variables to the model is small. Sparse methods for regression and classification are able to suppress these variables. This is possible by adding an appropriate penalty term to the objective function of the method. An overview of recent sparse methods for regression and classification is provided. The methods are applied to several high-dimensional data sets from chemometrics. A comparison with the non-sparse counterparts allows us to acquire an insight into their performance. Copyright © 2012 John Wiley & Sons, Ltd.

106 citations

Proceedings ArticleDOI
20 Jun 1995
TL;DR: A multiclass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples are presented.
Abstract: We present a self-organizing framework called the SHOSLIF-M for learning and recognizing spatiotemporal events (or patterns) from intensity image sequences. The proposed framework consists of a multiclass, multivariate discriminant analysis to automatically select the most discriminating features (MDF), a space partition tree to achieve a logarithmic retrieval time complexity for a database of n items, and a general interpolation scheme to do view inference and generalization in the MDF space based on a small number of training samples. The system is tested to recognize 28 different hand signs. The experimental results show that the learned system can achieve a 96% recognition rate for test sequences that have not been used in the training phase. >

106 citations

Journal ArticleDOI
TL;DR: In this article, the combination of mid infrared spectroscopy and multivariate analysis was explored as a tool to classify commercial wines sourced from organic (ORG) and non-organic (NORG) production systems.

106 citations


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