Fisher Discriminant Analysis Algorithm Research Articles

Simultaneous identification and differentiation of human fingernails and hair from different regions by Terahertz time-domain spectroscopy and chemometrics for forensic purpose

Hair and fingernails, emerging as crucial biological physical evidence, play a prominent role in geographical traceability studies within the forensic anthropology. This study explored the potential of terahertz spectroscopy and chemometrics techniques in identifying hair and fingernail samples from various geographical regions. A total of 196 hair and fingernail samples underwent scanning with terahertz time-domain spectroscopy. Different chemometric tools, including multilayer perceptron, support vector machine, and Fisher discriminant analysis, were employed for model building on fingernail dataset, hair dataset, and fusion data, respectively. The performance of models constructed based on different datasets was compared and evaluated using metrics such as accuracy, precision, recall, and F-score. Results revealed that the fusion data, employing the Fisher discriminant analysis (FDA) algorithm, exhibited the best recognition ability, achieving a 100 % accuracy rate and F-value. Terahertz spectroscopy combined with FDA algorithm emerged as an effective and convenient tool for differentiating hair and fingernail samples from diverse geographical regions. This innovative technique boasts environmental friendliness, rapid analyses, and minimal sample preparation requirements compared to traditional methods.

An improved Fisher discriminant analysis algorithm based on Procrustes analysis for adaptive fault recognition

Aiming at the problem of continuous model updating for fault recognition in the time-varying process, a novel method called the Procrustes analysis–based Fisher discriminant analysis was proposed. First, each class of the training data was preprocessed by Procrustes analysis. Second, the new test data were aligned with each class of the training data by Procrustes analysis. Then, all the data were reduced to a low-dimensional space using Fisher discriminant analysis. Finally, the Euclidean distance between the test data and the training data after the Procrustes analysis was calculated, and the class recognition was achieved based on the discriminant principle of Fisher discriminant analysis. Two case studies show that the proposed Procrustes analysis–based Fisher discriminant analysis is superior to the traditional method based on Fisher discriminant analysis, and it can be used for fault recognition in a new and efficient way.

A nested-loop Fisher discriminant analysis algorithm

Fisher discriminant analysis (FDA), as a very important method for feature extraction, has been widely used in different applications. However, some drawbacks of the conventional FDA algorithm have limited its success and applications. In order to improve the discriminant power, a new discriminant analysis algorithm is proposed based on Fisher's linear discriminant objective by developing a nested loop algebra, called nested-loop Fisher discriminant analysis (NeLFDA). The basic idea of the proposed NeLFDA is to overcome three important problems of the conventional Fisher discriminant analysis algorithm: (1) the within-class scatter matrix may be singular for eigenvalue decomposition, (2) the number of extracted discriminant components is limited by rank deficiency of the between-class scatter matrix, and (3) the discriminant components are correlated with each other for each class. The above problems are addressed in a nested-loop iterative process including inner-loop and outer-loop calculations. Using the proposed algorithm, its application to classification and fault diagnosis is evaluated on two examples. Illustration results show that the proposed algorithm can better separate different classes with improved discriminant power and provide more promising fault diagnosis performance.

Manifold Adaptive Kernel Local Fisher Discriminant Analysis for Face Recognition

To efficiently cope with the high dimensionalities and complex nonlinear variations of face images in face recognition task, a novel manifold adaptive kernel local Fisher discriminant analysis algorithm is proposed in this paper. The core idea of this algorithm is as follows: First, the local manifold structure of the face image is modeled by a nearest neighbor graph. Then, an original input kernel function is deformed with respect to the local manifold structure. Finally, the resulting manifold adaptive kernel function is incorporated into the kernel local Fisher discriminant analysis(LFDA) method, which leads to the manifold adaptive kernel LFDA(MAKL) algorithm for face recognition. Experimental results on three popular face databases show that the proposed algorithm performs much better than other related algorithms.

COMPREHENSIVE ANALYSIS FOR THE LOCAL FISHER DISCRIMINANT ANALYSIS

Using local data information, the recently proposed local Fisher Discriminant Analysis (LFDA) algorithm18 provides a new way of handling the multimodal issues within classes where the conventional Fisher Discriminant Analysis (FDA) algorithm fails. Like the FDA algorithm (global counterpart), the LFDA suffers when it is applied to the higher dimensional data sets. In this paper, we propose a new formulation by which a robust algorithm can be formed. The new algorithm offers more robust results for higher dimensional data sets when compared with the LFDA in most cases. By extensive simulation studies, we have demonstrated the practical usefulness and robustness of our new algorithm in data visualization.

Time-resolved autofluorescence spectroscopy for classifying normal and premalignant oral tissues

Time-resolved autofluorescence spectroscopy has been used for effectively distinguishing normal tissues from precancers and cancers in various organs. The aim of this study was to find out the possibility of using time-resolved autofluorescence spectroscopy to differentiate normal oral mucosa (NOM) from oral premalignant lesions including verrucous hyperplasia (VH), epithelial hyperplasia (EH), and epithelial dysplasia (ED). Time-resolved autofluorescence spectra at 633 nm under 410-nm excitation were recorded for 15 VH, 9 EH, 14 ED, and 38 NOM samples. The two-component lifetimes of the obtained curves were calculated, and a Fisher's discriminant analysis (FDA) was employed for distinguishing these tissue samples. After two-component lifetimes for all samples being calculated, a two-dimensional scatter plot was developed, in which 76 oral tissue samples were separated into three groups by FDA. With a leave-one-out method, the FDA algorithm gave an accuracy rate of 93% for ED, of 75% for VH and EH, and of 100% for NOM samples. In addition, all oral premalignant lesions (including VH, EH, and ED) could be distinguished from NOM samples by this FDA algorithm. We conclude that time-resolved autofluorescence spectroscopy at 633 nm under 410-nm excitation, based on two-component lifetime calculation and FDA, is a very sensitive technique for in vivo diagnosis of oral premalignant lesions. .