Ranking 2DLDA features based on fisher discriminance

Abstract

In classification of matrix-variate data, two-directional linear discriminant analysis (2DLDA) methods extract discriminant features while preserving and utilizing the matrix structure. These methods provide computational efficiency and improved performance in small sample size problems. Existing 2DLDA solutions produce a feature matrix which is commonly vectorized for processing by conventional vectorbased classifiers. However, the vectorization step requires a one-dimensional ranking of features according to their discriminance power. We first demonstrate that independent column-wise and row-wise ranking provided by 2DLDA is not sufficient for uniquely sorting the resulting features, and does not guarantee the selection of the most discriminant features. Then, we theoretically derive the desired global ranking score based on Fisher's criterion. The current results focus on non-iterative solutions, but future extensions to iterative 2DLDA variants are possible. Face recognition experiments using images from the PIE data set are used to demonstrate the theoretically proved improvements over the existing solutions.

Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

“…To increase the discriminant potential, those variables that obtained better results with respect to an objective function were selected. A total of four filters were used: Fisher’s score [ 25 , 26 ], ReliefF [ 27 , 28 ], Chi-square [ 29 ], and MRMR [ 30 , 31 ] (see Appendix B for a description of filters ). Applying the four filters , four scores and ranking positions were obtained for each feature.…”

“…To increase the discriminant potential, those variables that obtained better results with respect to an objective function were selected. A total of four filters were used: Fisher's score [25,26], ReliefF [27,28], Chi-square [29], and MRMR [30,31] (see Appendix B for In order to optimize the performance of the algorithms and make their training more efficient, the descriptors were subjected to a normalization process using the z-score method [22][23][24].…”

The improved (2 D ) 2 PCA algorithm and its parallel implementation based on image block

“…The research of twodimensional linear discriminant analysis (2DLDA), i.e. the parallel promotion of LDA in matrix mode is referred to reference [11][12][13] . The essence of 2DPCA equaled to the row block of PCA [14] .…”