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Functional Linear Discriminant Analysis for Irregularly Sampled Curves
Classification Filtering Functional data Linear discriminant analysis Low dimensional representation Reduced rank Regularized discriminant analysis Sparse curves
2015/8/21
We introduce a technique for extending the classical method of Linear Discriminant Analysis to data sets where the predictor variables are curves or functions. This procedure, which we call functional...
Feature Extraction for Nonparametric Discriminant Analysis
ClassiŽ cation Density estimation Dimension reduction LDA Projection pursuit Reduced-rankmodel SAVE
2015/8/21
In high-dimensional classification problems, one is often interested in ?? finding a few important discriminant directions in order to reduce the dimensionality.Fisher’s linear discriminant analysis(L...
Regularized linear discriminant analysis and its application in microarrays
Regularized linear discriminant analysis microarrays
2015/8/21
In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “near...
We consider the problem of performing interpretable classification in the high-dimensional setting, in which the number of features is very large and the number of observations is limited. This settin...
A Direct Estimation Approach to Sparse Linear Discriminant Analysis
Classification constrained l1-minimization Fisher’s rule linear discriminant analysis naive Bayes rule sparsity
2011/10/8
Abstract: This paper considers sparse linear discriminant analysis of high-dimensional data. In contrast to the existing methods which are based on separate estimation of the precision matrix $\O$ and...
Ordinal Discriminant Analysis: A new approach to the construction of optimal linear scores for ordinal risk categories
construction optimal linear scores ordinal risk categories
2011/3/4
Most classification methods provide either a prediction of group membership or an assessment of class membership probability. In the case of two-group classifica-tion the predicted probability can be ...
Ordinal Discriminant Analysis: A new approach to the construction of optimal linear scores for ordinal risk categories
Ordinal Discriminant Analysis optimal linear scores ordinal risk categories
2011/1/4
Most classification methods provide either a prediction of group membership or an assessment of class membership probability. In the case of two-group classification the predicted probability can be d...
The extended data envelopment analysis/discriminant analysis approach of fuzzy models
Data Envelopment Analysis/ Discriminant Analysis fuzzy numbers
2010/9/13
Extended Data Envelopment Analysis/Discriminant Analysis (DEA/DA), is a widely applied approach for firstly the classification of observations into groups and secondly the prediction of the membership...
The extended data envelopment analysis/discriminant analysis approach of models with undetermined restrictions
Extended data envelopment analysis (DEA)-discriminant analysis probability, linear programming
2010/9/13
To represent an optimization problem as a linear programming (LP), there are several assumptions needed. Among them, there is the ”Deterministic” assumption that requires all the coefficients of the L...
Data envelopment analysis technique which is developed based on the mathematical programming, evaluates the relative efficiency of a set of homogeneous decision making units. This paper shows the meth...