PSO-Based Uncorrelated Hybrid Discriminant Analysis Algorithm

2011 ◽  
Vol 109 ◽  
pp. 671-675 ◽  
Author(s):  
Xiao Ping Liu ◽  
Gui Yun Xu
Keyword(s):  
Discriminant Analysis ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Principal Component ◽  
Small Sample ◽  
Model Parameters ◽  
Orthogonal Condition ◽  
Linear Discriminant ◽  
Analysis Algorithm ◽  
Discriminant Information

Hybrid discriminant analysis (HDA) can overcome small sample problems and outperform PCA and LDA by unifying principal component analysis (PCA) and linear discriminant analysis (LDA) in a single framework. However, the existing HDA algorithm can’t extract more discriminant information from dataset, and model parameters are difficult to select. To deal with the above problems, a particle swarm optimal (PSO)-based uncorrelated hybrid discriminant analysis algorithm is presented. The conjugate orthogonal condition is added to optimization problem of HDA, PSO is explored to select optimal HDA parameters and the optimal solution can be achieved by solving eigenvalue problem. Simulation demonstrates merits of the proposed algorithm.

Classification of Apple Using near Infrared Spectroscopy Based on Fuzzy Discriminant Analysis

2013 ◽  
Vol 710 ◽  
pp. 524-528 ◽  
Author(s):  
Xiao Hong Wu ◽  
Xing Xing Wan ◽  
Bin Wu ◽  
Fan Wu
Keyword(s):  
Infrared Spectroscopy ◽  
Discriminant Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Discriminant Information

Classification of apple is an important link in postharvest commercialization processing. To realize the non-destructive, rapid and effective discrimination of apple fruits, the near infrared reflectance spectra of four varieties of apples were collected using near infrared spectroscopy, reduced by principal component analysis (PCA) and used to extract the discriminant information by linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), fuzzy discriminant analysis (FDA) and Foley-Sammon discriminant analysis. Finally k-nearest neighbor finished the classification. The classification results showed that FDA could extract the discriminant information of NIR spectra more effectively, and achieved the highest classification accuracy.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

scholarly journals Determination of HPLC-UV Fingerprints of Spanish Paprika (Capsicum annuum L.) for Its Classification by Linear Discriminant Analysis

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4479 ◽  
Author(s):  
Xavier Cetó ◽  
Núria Serrano ◽  
Miriam Aragó ◽  
Alejandro Gámez ◽  
Miquel Esteban ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Principal Component ◽  
Reversed Phase ◽  
Classification Rate ◽  
Phenolic Profile ◽  
Linear Discriminant ◽  
Sample Extraction

The development of a simple HPLC-UV method towards the evaluation of Spanish paprika’s phenolic profile and their discrimination based on the former is reported herein. The approach is based on C18 reversed-phase chromatography to generate characteristic fingerprints, in combination with linear discriminant analysis (LDA) to achieve their classification. To this aim, chromatographic conditions were optimized so as to achieve the separation of major phenolic compounds already identified in paprika. Paprika samples were subjected to a sample extraction stage by sonication and centrifugation; extracting procedure and conditions were optimized to maximize the generation of enough discriminant fingerprints. Finally, chromatograms were baseline corrected, compressed employing fast Fourier transform (FFT), and then analyzed by means of principal component analysis (PCA) and LDA to carry out the classification of paprika samples. Under the developed procedure, a total of 96 paprika samples were analyzed, achieving a classification rate of 100% for the test subset (n = 25).

Information Theoretic Learning

2011 ◽  
pp. 902-909 ◽  
Author(s):  
Deniz Erdogmus ◽  
Jose C. Principe
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Adaptive Systems ◽  
Mean Squared Error ◽  
Learning Algorithms ◽  
Optimal Solution ◽  
Principal Component ◽  
Second Order ◽  
Linear Discriminant ◽  
Non Gaussian

Learning systems depend on three interrelated components: topologies, cost/performance functions, and learning algorithms. Topologies provide the constraints for the mapping, and the learning algorithms offer the means to find an optimal solution; but the solution is optimal with respect to what? Optimality is characterized by the criterion and in neural network literature, this is the least addressed component, yet it has a decisive influence in generalization performance. Certainly, the assumptions behind the selection of a criterion should be better understood and investigated. Traditionally, least squares has been the benchmark criterion for regression problems; considering classification as a regression problem towards estimating class posterior probabilities, least squares has been employed to train neural network and other classifier topologies to approximate correct labels. The main motivation to utilize least squares in regression simply comes from the intellectual comfort this criterion provides due to its success in traditional linear least squares regression applications – which can be reduced to solving a system of linear equations. For nonlinear regression, the assumption of Gaussianity for the measurement error combined with the maximum likelihood principle could be emphasized to promote this criterion. In nonparametric regression, least squares principle leads to the conditional expectation solution, which is intuitively appealing. Although these are good reasons to use the mean squared error as the cost, it is inherently linked to the assumptions and habits stated above. Consequently, there is information in the error signal that is not captured during the training of nonlinear adaptive systems under non-Gaussian distribution conditions when one insists on second-order statistical criteria. This argument extends to other linear-second-order techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and canonical correlation analysis (CCA). Recent work tries to generalize these techniques to nonlinear scenarios by utilizing kernel techniques or other heuristics. This begs the question: what other alternative cost functions could be used to train adaptive systems and how could we establish rigorous techniques for extending useful concepts from linear and second-order statistical techniques to nonlinear and higher-order statistical learning methodologies?

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