Results 1 
5 of
5
Symmetric Two Dimensional Linear Discriminant Analysis (2DLDA)
"... Linear discriminant analysis (LDA) has been successfully applied into computer vision and pattern recognition for effective feature extraction. Highdimensional objects such as images are usually transform as 1D vectors before the LDA transformation. Recently, twodimension LDA (2DLDA) methods have ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Linear discriminant analysis (LDA) has been successfully applied into computer vision and pattern recognition for effective feature extraction. Highdimensional objects such as images are usually transform as 1D vectors before the LDA transformation. Recently, twodimension LDA (2DLDA) methods have been proposed which reduced the dimensionality of images without transforming the matrices into vectors. However, the objective function for 2DLDA remains an unresolved problem. In this paper, we (1) propose a symmetric LDA formulation which resolves the ambiguity problem, and (2) propose an effective algorithm to solve the symmetric 2DLDA objective. Experiments on UMIST, CMU PIE, and YaleB images databases show that our approach outperforms the other 2DLDA methods in terms of both classification accuracy and objective function results. 1.
Regularized lda based on separable scatter matrices for classification of spatiospectral EEG patterns
 In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP
, 2013
"... Linear discriminant analysis (LDA) is a commonlyused feature extraction technique. For matrixvariate data such as spatiospectral electroencephalogram (EEG), matrixvariate LDA formulations have been proposed. Compared to the standard vectorvariate LDA, these formulations assume a separable stru ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Linear discriminant analysis (LDA) is a commonlyused feature extraction technique. For matrixvariate data such as spatiospectral electroencephalogram (EEG), matrixvariate LDA formulations have been proposed. Compared to the standard vectorvariate LDA, these formulations assume a separable structure for the withinclass and betweenclass scatter matrices; these structured parameters can be estimated more accurately with a limited number of training samples. However, separable scatters do not fit some data, resulting in aggravated performance for matrixvariate methods. This paper first proposes a common framework for the vectorvariate LDA with nonseparable scatters and our previously proposed solution with separable scatters. Then, a regularization of the nonseparable scatter estimates toward the separable estimates is introduced. This novel regularized framework integrates vectorvariate and matrixvariate approaches, and allows the estimated scatter matrices to adapt to the data characteristics. Experiments on data set V from BCI competition III demonstrate that the proposed framework achieves a considerable classification performance gain. Index Terms — regularization, separable covariance, matrixvariate Gaussian, linear discriminant analysis, 2DLDA. 1.
Computational Statistics and Data Analysis Separable linear discriminant analysis
, 2012
"... a b s t r a c t Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several twodimensional LDA (2DLDA) methods have been proposed in recent years. Among which, the Y2DLDA m ..."
Abstract
 Add to MetaCart
(Show Context)
a b s t r a c t Linear discriminant analysis (LDA) is a popular technique for supervised dimension reduction. Due to the curse of dimensionality usually suffered by LDA when applied to 2D data, several twodimensional LDA (2DLDA) methods have been proposed in recent years. Among which, the Y2DLDA method, introduced by Ye et al.