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Nonlinear multiclass discriminant analysis

Junshui Ma, J.L. Sancho-Gomez, S.C. Ahalt
2003 IEEE Signal Processing Letters  
An alternative nonlinear multiclass discriminant algorithm is presented.  ...  Index Terms-Discriminant analysis, feature extraction, kernel method.  ...  GDA The linear discriminant analysis for multiclass problems described above can be combined with the kernel trick [7] to obtain a nonlinear multiclass discriminant analysis.  ... 
doi:10.1109/lsp.2003.813680 fatcat:4kx7go2ysfdkroti2nhylcf2au

Kernel-based nonlinear discriminant analysis using minimum squared errors criterion for multiclass and undersampled problems

Wen-Jun Zeng, Xi-Lin Li, Xian-Da Zhang, En Cheng
2010 Signal Processing  
In this paper, a new kernel-based nonlinear discriminant analysis algorithm using minimum squared errors criterion (KDA-MSE) is proposed to solve these two problems.  ...  It is well known that there exist two fundamental limitations in the linear discriminant analysis (LDA).  ...  The generalized discriminant analysis (GDA) [19] is applicable to multiclass problem.  ... 
doi:10.1016/j.sigpro.2009.06.002 fatcat:3w5jvlzbujbyfmeb3dcmqexltq

Multiclass discrimination of cervical precancers using Raman spectroscopy

Elizabeth M. Kanter, Shovan Majumder, Elizabeth Vargis, Amy Robichaux-Viehoever, Gary J. Kanter, Heidi Shappell, Howard W. Jones III, Anita Mahadevan-Jansen
2009 Journal of Raman Spectroscopy  
) to classify spectra based on nonlinear features for multiclass analysis of Raman spectra.  ...  Algorithms like linear discriminant analysis (LDA) are incapable of differentiating among three or more types of tissues.  ...  Statistical analysis -multiclass MRDF combined with SMLR was used to develop a multiclass diagnostic algorithm.  ... 
doi:10.1002/jrs.2108 pmid:21691450 pmcid:PMC3117583 fatcat:4kfbnxmgv5e7ffrewrrlcrc5te

Multiclass classifiers based on dimension reduction with generalized LDA

Hyunsoo Kim, Barry L. Drake, Haesun Park
2007 Pattern Recognition  
Linear discriminant analysis (LDA) has been widely used for dimension reduction of data sets with multiple classes.  ...  A marginal linear discriminant classifier, a Bayesian linear discriminant classifier, and a onedimensional Bayesian linear discriminant classifier are introduced for multiclass classification.  ...  Fisher's linear discriminant analysis (FDA) [4] was developed for dimension reduction of binary class problems and its extension to multiclass is generally referred to as Linear Discriminant Analysis  ... 
doi:10.1016/j.patcog.2007.03.002 fatcat:4digsv6axzayhmay3w44m7kgni

Classification of hyperspectral remote sensing images with support vector machines

F. Melgani, L. Bruzzone
2004 IEEE Transactions on Geoscience and Remote Sensing  
First, we propose a theoretical discussion and experimental analysis aimed at understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces.  ...  Finally, we study the potentially critical issue of applying binary SVMs to multiclass problems in hyperspectral data.  ...  SVMs in the analysis of hyperspectral data.  ... 
doi:10.1109/tgrs.2004.831865 fatcat:bakdebbxnnflfn2gs6k3jrgqki

Class-Specific Kernel-Discriminant Analysis for Face Verification

Georgios Goudelis, Stefanos Zafeiriou, Anastasios Tefas, Ioannis Pitas
2007 IEEE Transactions on Information Forensics and Security  
analysis (KDDA), complete kernel Fisher's discriminant analysis (CKFDA), the two-class KDDA, CKFDA, and other two-class and multiclass variants of kernel-discriminant analysis based on Fisher's criterion  ...  The typical Fisher's linear discriminant analysis (FLDA) gives only one or two projections in a two-class problem. This is a very strict limitation to the search of discriminant dimensions.  ...  to the between class scatter matrix; (c) proposed kernel-discriminant analysis with only the first dimension; and (d) proposed kernel-discriminant analysis with 100 dimensions.  ... 
doi:10.1109/tifs.2007.902915 fatcat:526j37dyc5agdesvpb37nlkdqe

Functional connectivity-based signatures of schizophrenia revealed by multiclass pattern analysis of resting-state fMRI from schizophrenic patients and their healthy siblings

Yang Yu, Hui Shen, Huiran Zhang, Ling-Li Zeng, Zhimin Xue, Dewen Hu
2013 BioMedical Engineering OnLine  
Results: Our multiclass pattern analysis achieved 62.0% accuracy via leave-one-out cross-validation (p < 0.001).  ...  The objective of this study was to use multiclass pattern analysis to investigate the inheritable characters of schizophrenia at the individual level, by comparing whole-brain resting-state functional  ...  Discussion Multiclass pattern analysis To the best of our knowledge, our study provided a novel use for a multiclass pattern analysis method based on rs-fMRI to investigate the functional connectivity  ... 
doi:10.1186/1475-925x-12-10 pmid:23390976 pmcid:PMC3577608 fatcat:z7uxsxmdvzdkbcpzczrg643nwq

Fault diagnosis via structural support vector machines

Yi Peng, Qixiang Ye, Jianbin Jiao, Xiaogang Chen, Lijun Wu
2012 2012 IEEE International Conference on Mechatronics and Automation  
Discriminative methods are becoming more and more popular on fault diagnosis systems, while they need additional strategies or multiple models to cope with the multiple classification problems.  ...  In this paper, we introduce the structural Support Vector Machines (structural SVMs) to fault diagnosis, which can indentify multiple kinds of faults with only one uniform discriminative model.  ...  (PCA), independent component analysis (ICA), Fisher discriminant analysis (FDA) and partial least square (PLS).  ... 
doi:10.1109/icma.2012.6284371 fatcat:s3zmer4v6ndtrdaxfpy54wqszu

Towards Optimal Discriminating Order for Multiclass Classification

Dong Liu, Shuicheng Yan, Yadong Mu, Xian-Sheng Hua, Shih-Fu Chang, Hong-Jiang Zhang
2011 2011 IEEE 11th International Conference on Data Mining  
In this paper, we investigate how to design an optimized discriminating order for boosting multiclass classification.  ...  The main idea is to optimize a binary tree architecture, referred to as Sequential Discriminating Tree (SDT), that performs the multiclass classification through a hierarchical sequence of coarse-to-fine  ...  In this work, we propose a new algorithm towards the optimal discriminating order in multiclass discriminating procedure.  ... 
doi:10.1109/icdm.2011.147 dblp:conf/icdm/LiuYMHCZ11 fatcat:tgts5yk5ynbgxjg6bmpob7775y

Performance Analysis of Hybrid (supervised and unsupervised) method for multiclass data set

Rahul R.Chakre, Dr. Radhakrishna Naik
2014 IOSR Journal of Computer Engineering  
Experiments are performed for various two classes as well as multiclass dataset and performance of hybrid, standalone approaches are compared.  ...  Due to the increasing demand for multivariate data analysis from the various application the dimensionality reduction becomes an important task to represent the data in low dimensional space for the robust  ...  Limitations for the PCA It works very undesirable for the nonlinear dataset. Due to this classification accuracy is drastically reduced. But for that nonlinear component analysis is used i.e.  ... 
doi:10.9790/0661-16439399 fatcat:3nysok3eefcjhiuibjlcr4crxy

Learning Object Material Categories via Pairwise Discriminant Analysis

Zhouyu Fu, Antonio Robles-Kelly
2007 2007 IEEE Conference on Computer Vision and Pattern Recognition  
In this paper, we investigate linear discriminant analysis (LDA) methods for multiclass classification problems in hyperspectral imaging.  ...  As a result, we present a pairwise discriminant analysis algorithm for learning class categories.  ...  We call this new formulation of discriminant analysis Pairwise Discriminant Analysis (PDA).  ... 
doi:10.1109/cvpr.2007.383458 dblp:conf/cvpr/FuR07 fatcat:5fdbxax54raotlpqyiox44t4ki

Discriminant Analysis for Fast Multiclass Data Classification Through Regularized Kernel Function Approximation

Santanu Ghorai, Anirban Mukherjee, Pranab K Dutta
2010 IEEE Transactions on Neural Networks  
In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation (VVRKFA).  ...  The results indicate the significant improvement in both training and testing time compared to that of multiclass SVM with comparable testing accuracy principally in large data sets.  ...  Algorithms such as linear discriminant analysis (LDA) [21, pp. 106-119] , nearest neighborhoods [21, pp. 463-483] , regression and decision trees including C4.5 [36] and CART [7] , neural networks  ... 
doi:10.1109/tnn.2010.2046646 pmid:20421179 fatcat:mohdvgh4jzailagya6wvvajpni

A Modified AdaBoost Algorithm with New Discrimination Features for High-Resolution SAR Targets Recognition

Kun CHEN, Yuehua LI, Xingjian XU, Yuanjiang LI
2015 IEICE transactions on information and systems  
The Ada MCBoost algorithm is then proposed to classify multiclass SAR targets.  ...  In the new algorithm, we introduce a novel large-margin loss function to design a multiclass classifier directly instead of decomposing the multiclass problem into a set of binary ones through the error-correcting  ...  Generally, feature extraction methods are categorized as either linear and nonlinear. Principal component analysis (PCA) and linear discrimination analysis (LDA) [2] are two linear methods.  ... 
doi:10.1587/transinf.2015edl8090 fatcat:iniqkcxienenflkd6aybr2sjdi

ClassSPLOM – A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data [article]

Michael Aupetit, Ahmed Ali
2022 arXiv   pre-print
It uses the Scatterplot Matrix (SPLOM) metaphor to visualize a Linear Discriminant Analysis projection of the data for each pair of classes and a set of Receiving Operating Curves to evaluate their trustworthiness  ...  In multiclass classification of multidimensional data, the user wants to build a model of the classes to predict the label of unseen data.  ...  We propose to consider the linear classifier defined by Fisher Linear Discriminant Analysis (LDA) [6] as a basis for comparison to the possibly non-linear classifier used to get the confusion matrix.  ... 
arXiv:2201.12822v1 fatcat:fz26ppcy75fipixkgog56mrxcy

Dynamic fault diagnosis in chemical process based on SVM-HMM

Yi Peng, Xiaodan Zhang, Zhenjun Han, Jianbin Jiao
2013 2013 IEEE International Conference on Mechatronics and Automation  
Herein, SVM-HMM is a good method for dynamic continuous data which indentifies multiple kinds of faults with only one uniform discriminative model instead of multiple ones.  ...  [6] proposed a new nonlinear process monitoring method based on kernel principle component analysis (KPCA) , the experiments of which demonstrated that KPCA can effectively capture nonlinear relationships  ...  discriminant analysis (FDA) [4] and partial least square (PLS) [5] , have been successfully applied to process fault detection and diagnosis for some years.  ... 
doi:10.1109/icma.2013.6618169 fatcat:6juyf6tp7bcuhhj44z2cqhl2ou
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