Two-dimensional Discriminant Analysis Research Articles

Classification of oil pollutants based on excitation-emission matrix fluorescence spectroscopy and two-dimensional discriminant analysis.

Excitation-Emission Matrix (EEM) Fluorescence spectroscopy and two-dimensional (2D) discriminant analysis were used to classify oil pollutants. Firstly, the EEM fluorescence spectroscopy of the oil samples was collected using the FS920 steady-state fluorescence spectrometer, and EEM was preprocessed by removing scattering and normalization. Secondly, EEM was analyzed and characterized by parallel factor analysis (PARAFAC). Finally, all the collected samples were divided into training and test sets by the Kennard-Stone algorithm. The classification models of the training set samples were established by using 2D-PCA, 2D-LDA, PARAFAC-2DPCA, PARAFAC-2DLDA, PARAFAC-LDA and NPLS-DA algorithms, respectively. These models were then used to classify test set samples. The classification performance of the used models was assessed by accuracy, sensitivity and specificity. The best classification results in the used models were obtained by using 2D-PCA and 2D-LDA with 95% and 95% accuracy, respectively. These results provide an important reference for classification of oil pollutants.

Two-dimensional discriminant analysis based on Schatten p-norm for image feature extraction

A Schatten p-norm-based two-dimensional principal component analysis (2DPCA-SP) method was proposed for image feature extraction in our previous work. As an unsupervised method, 2DPCA-SP ignores the label information of training samples, which is essential to classification tasks. In this paper, we propose a novel Schatten p-norm-based two-dimensional discriminant analysis (2DDA-SP) method for image feature extraction, which learns an optimal projection matrix by maximizing the difference of Schatten p-norm-based between-class dispersion and Schatten p-norm-based within-class dispersion in low-dimensional feature space. By using both the Schatten p-norm metric and the label information of training samples, 2DDA-SP not only can efficiently extract discriminative features, but is also robust to outliers. We also propose an efficient iterative algorithm to solve the optimization problem of 2DDA-SP with 0<p<1. Experimental results on several image databases show that 2DDA-SP with 0<p<1 is effective and robust for image feature extraction.

A note on two-dimensional linear discriminant analysis

2DLDA and its variants have attracted much attention from researchers recently due to the advantages over the singularity problem and the computational cost. In this paper, we further analyze the 2DLDA method and derive the upper bound of its criterion. Based on this upper bound, we show that the discriminant power of two-dimensional discriminant analysis is not stronger than that of LDA under the assumption that the same dimensionality is considered. In experimental parts, on one hand, we confirm the validity of our claim and show the matrix-based methods are not always better than vector-based methods in the small sample size problem; on the other hand, we compare several distance measures when the feature matrices and feature vectors are applied. The matlab codes used in this paper are available at http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do?objectType=category&objectId=127&objectName=Application.

Strategies for improving the diagnostic specificity of the frequency doubling perimeter

To evaluate various strategies designed to improve the specificity of the interpretation of results obtained with the frequency doubling technology perimeter (FDT) used in the full-threshold mode. Three different strategies were compared using data from 452 glaucoma patients and 237 healthy subjects: combining several FDT parameters from a single test, combining the FDT test with a GDx test, and confirming an abnormal FDT test result with a repeat test. Confirming an abnormal FDT test result with a repeat test yielded a specificity increase of 0.10, from 0.80 to 0.90, at the expense of some loss of sensitivity for early but not for moderate or severe glaucoma. Combining several FDT parameters from a single test and combining FDT with GDx did not yield any noticeable increase in diagnostic performance. A modest increase in FDT diagnostic performance can be obtained by the confirmation of an abnormal test result with a repeat test.

Separation of pulmonary disorders with two-dimensional discriminant analysis of crackles

Previous studies have indicated that disorders producing crackling lung sounds may be different in terms of the waveform of the crackles or their timing in a respiratory cycle. In this study, we evaluated whether two-dimensional discriminant analysis of crackles has a better ability to separate pulmonary disorders than does a single-dimensional analysis. Cracking sounds of patients with cryptogenic fibrosing alveolitis (n = 10), bronchiectasis (n = 10), COPD (n = 10), heart failure (n = 10) and acute pneumonia (n = 11) and of those recovering from pneumonia (n = 9) have been studied. Variables indicating the timing of crackles during inspiration (beginning and endpoint of crackling) and their waveform (initial deflection width (IDW), two cycle duration (2CD) and largest deflection width (LDW)), were used for the analysis. The discrimination properties of one- and two-dimensional analyses with these variables were compared. The two-dimensional distances between the patient groups were the largest by combining IDW and the end-point of crackling. Cryptogenic fibrosing alveolitis was distinguished from bronchiectasis, COPD, heart failure and acute pneumonia without overlap. The differences between the diseases were illustrated two-dimensionally with ellipses. The two-dimensional analysis resulted in better separation between the groups than the use of single characteristics alone. This type of analysis can enhance the diagnostic power of acoustic pulmonary studies. It is also an informative visual way to find differences among pulmonary disorders.