Robust linear discriminant analysis for chemical pattern recognition

Improved PCA + LDA Applies to Gastric Cancer Image Classification Process

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

Neptunia oleracea is a plant consumed as a vegetable and which has been used as a folk remedy for several diseases. Herein, two regression models (partial least squares, PLS; and random forest, RF) in a metabolomics approach were compared and applied to the evaluation of the relationship between phenolics and bioactivities of N. oleracea. In addition, the effects of different extraction conditions on the phenolic constituents were assessed by pattern recognition analysis. Comparison of the PLS and RF showed that RF exhibited poorer generalization and hence poorer predictive performance. Both the regression coefficient of PLS and the variable importance of RF revealed that quercetin and kaempferol derivatives, caffeic acid and vitexin-2-O-rhamnoside were significant towards the tested bioactivities. Furthermore, principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) results showed that sonication and absolute ethanol are the preferable extraction method and ethanol ratio, respectively, to produce N. oleracea extracts with high phenolic levels and therefore high DPPH scavenging and α-glucosidase inhibitory activities. Both PLS and RF are useful regression models in metabolomics studies. This work provides insight into the performance of different multivariate data analysis tools and the effects of different extraction conditions on the extraction of desired phenolics from plants. © 2017 Society of Chemical Industry.

Face recognition using direct, weighted linear discriminant analysis and modular subspaces

Structure–activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines

Background: Linear Discriminant Analysis (LDA) is a powerful and widely used technique for classification with correlated variables. Principal Components (PCs) group these variables into linear combinations and produce independent variables. The LDA on these PC’s may provide better classification accuracy in clinical diagnostics than on usual measurements. Methodology: Two datasets were utilized for demonstration: one from a Sudden Sensorineural Hearing Loss (SSNHL) case-control study and the other from a Gall Bladder (GB) case-control study. Linear Discriminant Analysis (LDA) was conducted on the actual correlated measured variables for group classification, as well as on the derived principal component variables, to compare their classification accuracies. Performance metrics including Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Classification Accuracy, and F1 Score were assessed. For validation, a third simulated dataset was employed. Additionally, LDA was performed on each dataset using eigenvectors of the control group applied to the cases and vice versa, revealing a strong agreement in classification as measured by the kappa statistic. Results: When LDA was applied to the actual lipid measurements in the SSNHL dataset, the classification accuracy was 57.2%, and the F1 score was 39.7%. However, when LDA was performed using principal components (PCs), the classification accuracy markedly improved to 99.2%, with an F1 score of 98.5%. Similarly, for the GB cancer dataset, the classification accuracy and F1 score were initially 77.2% and 77.3%, respectively. Upon applying LDA with the PCs, these metrics were significantly enhanced to 98.4% and 98.3%, respectively. For the simulated dataset, both the classification accuracy and F1 score were 99.1%. The study also demonstrated that the classification accuracy and F1 score remained consistent regardless of whether the eigenvectors from the cases or controls were used to classify new subjects (Kappa Statistic = 0.962, P < 0.001). Conclusion: In group separation, utilizing principal components significantly improves classification accuracy and overall performance metrics, outperforming the use of the original correlated predictors.

Recently, there has been a surge in the development of robust norm distance-based linear discriminant analysis (LDA) techniques, which have garnered significant attention in the field of feature extraction. However, a persistent issue that has yet to be resolved is that the successful suppression of outliers may inadvertently impede the accurate discrimination of normal points. To solve this problem, we, in this article, study a novel robust LDA measured by double capped $L_{p}$ -norm distance (CLD) metrics with min constraints (DCLDA) to learn robust discriminant projections, in which normal points and outliers are separately treated. To be specific, it takes a double capped $L_{p}$ -norm with "Min" constraints in the proposed model to measure the distances for between- and within-class dispersions. The proposed model effectively ensures accurate discrimination of normal points by $L_{p}$ -norm, while also eliminating the exaggerated effect of outliers that may arise from larger $p$ values. The resulted objective is not trivial because of its nonconvexity and nonsmoothness. As one of the major contributions of this article, we introduce a new reformulation that provides an objective problem theoretically equivalent to the original. By this reformulation, we develop an effective iterative algorithm to solve the proposed model. The algorithm is proven to be convergent through rigorous theoretical analysis. Extensive experiments were conducted on several real-world datasets across different image classification tasks to showcase the effectiveness of the proposed method.

Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features, The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the `2;1 constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

Characterization of acoustic emission analysis in applications for inhalation device performance assessment

Modification of poly(L-lactic acid)-based films and evaluation of physical and antibacterial properties by using multivariate data analysis

Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients

Separable linear discriminant analysis

Plants perceive pathogenic threats from the environment that have evaded preformed barriers through pattern recognition receptors (PRRs) that recognise microbe-associated molecular patterns (MAMPs). The perception of and triggered defence to lipopolysaccharides (LPSs) as a MAMP is well-studied in mammals, but little is known in plants, including the PRR(s). Understanding LPS-induced secondary metabolites and perturbed metabolic pathways in Arabidopsis will be key to generating disease-resistant plants and improving global plant crop yield. Recently, Arabidopsis LPS-binding protein (LBP) and bactericidal/permeability-increasing protein (BPI)-related proteins (LBP/BPI related-1) and (LBP/BPI related-2) were shown to perceive LPS from Pseudomonas aeruginosa and trigger defence responses. In turn, brassinosteroid insensitive 1 (BRI1)-associated receptor kinase 1 (BAK1) is a well-established co-receptor for several defence-related PRRs in plants. Due to the lack of knowledge pertaining to LPS perception in plants and given the involvement of the afore-mentioned proteins in MAMPs recognition, in this study, Arabidopsis wild type (WT) and mutant (lbr2-2 and bak1-4) plants were pressure-infiltrated with LPSs purified from Pseudomonas syringae pv. tomato DC3000 (Pst) and Xanthomonas campestris pv. campestris 8004 (Xcc). Metabolites were extracted from the leaves at four time points over a 24 h period and analysed by UHPLC-MS, generating distinct metabolite profiles. Data analysed using unsupervised and supervised multivariate data analysis (MVDA) tools generated results that reflected time- and treatment-related variations after both LPS chemotypes treatments. Forty-five significant metabolites were putatively annotated and belong to the following groups: glucosinolates, hydroxycinnamic acid derivatives, flavonoids, lignans, lipids, oxylipins, arabidopsides and phytohormones, while metabolic pathway analysis (MetPA) showed enrichment of flavone and flavanol biosynthesis, phenylpropanoid biosynthesis, alpha-linolenic acid metabolism and glucosinolate biosynthesis. Distinct metabolite accumulations depended on the LPS chemotype and the genetic background of the lbr2-2 and bak1-4 mutants. This study highlights the role of LPSs in the reprogramming Arabidopsis metabolism into a defensive state, and the possible role of LBR and BAK1 proteins in LPSs perception and thus plant defence against pathogenic bacteria.

This paper aims to understand how the discriminative information of neuronal population activity evolves and accumulates over time. We present two classes of approaches namely the probability-based and response-based approaches to predict the perceptual reports of a trained macaque monkey on a single-trial basis by integrating neural signals from multiple electrodes across time. We extend the probability-based integration originally using only the quadratic discriminant analysis (QDA) by considering also the linear discriminant analysis (LDA) and logistic regression methods. Furthermore, we introduce the response-based integration for the QDA, LDA and logistic regression methods. Experimental examples demonstrate the effectiveness of these approaches for determining the perceptual state of a brain under study by integrating its localized spatiotemporal neuronal activity.

A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.