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Nonlinear multiclass discriminant analysis
2003
IEEE Signal Processing Letters
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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
2010
Signal Processing
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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
2009
Journal of Raman Spectroscopy
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) 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
2007
Pattern Recognition
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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
2004
IEEE Transactions on Geoscience and Remote Sensing
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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
2007
IEEE Transactions on Information Forensics and Security
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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
2013
BioMedical Engineering OnLine
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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
2012
2012 IEEE International Conference on Mechatronics and Automation
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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
2011
2011 IEEE 11th International Conference on Data Mining
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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
2014
IOSR Journal of Computer Engineering
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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
2007
2007 IEEE Conference on Computer Vision and Pattern Recognition
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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
2010
IEEE Transactions on Neural Networks
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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
2015
IEICE transactions on information and systems
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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]
2022
arXiv
pre-print
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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
2013
2013 IEEE International Conference on Mechatronics and Automation
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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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