scholarly journals Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4834
Author(s):  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Francesc Pozo ◽  
Diego Tibaduiza
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Electronic Tongue ◽  
Locally Linear Embedding ◽  
Data Set ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

scholarly journals Locally Linear Embedding as Nonlinear Feature Extraction to Discriminate Liquids with a Cyclic Voltammetric Electronic Tongue

2021 ◽  
Vol 5 (1) ◽  
pp. 56
Author(s):  
Jersson X. Leon-Medina ◽  
Maribel Anaya ◽  
Diego A. Tibaduiza
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Electronic Tongue ◽  
Locally Linear Embedding ◽  
Cyclic Voltammetric ◽  
Linear Embedding ◽  
Voltammetric Electronic Tongue ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

Electronic tongues are devices used in the analysis of aqueous matrices for classification or quantification tasks. These systems are composed of several sensors of different materials, a data acquisition unit, and a pattern recognition system. Voltammetric sensors have been used in electronic tongues using the cyclic voltammetry method. By using this method, each sensor yields a voltammogram that relates the response in current to the change in voltage applied to the working electrode. A great amount of data is obtained in the experimental procedure which allows handling the analysis as a pattern recognition application; however, the development of efficient machine-learning-based methodologies is still an open research interest topic. As a contribution, this work presents a novel data processing methodology to classify signals acquired by a cyclic voltammetric electronic tongue. This methodology is composed of several stages such as data normalization through group scaling method and a nonlinear feature extraction step with locally linear embedding (LLE) technique. The reduced-size feature vector input to a k-Nearest Neighbors (k-NN) supervised classifier algorithm. A leave-one-out cross-validation (LOOCV) procedure is performed to obtain the final classification accuracy. The methodology is validated with a data set of five different juices as liquid substances.Two screen-printed electrodes voltametric sensors were used in the electronic tongue. Specifically the materials of their working electrodes were platinum and graphite. The results reached an 80% classification accuracy after applying the developed methodology.

Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning

2014 ◽  
Vol 533 ◽  
pp. 247-251
Author(s):  
Hai Bing Xiao ◽  
Xiao Peng Xie
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Extraction Method ◽  
Classification Performance ◽  
Locally Linear Embedding ◽  
Simulation Research ◽  
Feature Extraction Method ◽  
Linear Embedding ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

Kernel Orthogonal Neighborhood Preserving Discriminant Analysis

2011 ◽  
Vol 339 ◽  
pp. 571-574
Author(s):  
Xing Zhu Liang ◽  
Jing Zhao Li ◽  
Yu E Lin
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Extraction Method ◽  
Learning Algorithms ◽  
Recognition Rate ◽  
Kernel Matrix ◽  
Feature Extraction Method ◽  
Nonlinear Features ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

Several orthogonal feature extraction algorithms based on local preserving projection have recently been proposed. However, these methods still are linear techniques in nature. In this paper, we present nonlinear feature extraction method called Kernel Orthogonal Neighborhood Preserving Discriminant Analysis (KONPDA). A major advantage of the proposed method is that it is regarded every column of the kernel matrix as a corresponding sample. Then running KONPDA in kernel matrix, nonlinear features can be extracted. Experimental results on ORL database indicate that the proposed KONPDA method achieves higher recognition rate than the ONPDA method and other kernel-based learning algorithms.

Improved LLE Algorithm Based on Supervision

2013 ◽  
Vol 427-429 ◽  
pp. 1900-1902
Author(s):  
Li Li Gan ◽  
Si Si Chen ◽  
Juan Juan Cao ◽  
Zhong Yong Wu
Keyword(s):  
Manifold Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Locally Linear Embedding ◽  
Classification Problems ◽  
Series Of Experiments ◽  
Linear Embedding ◽  
Classification Information ◽  
Locally Linear

It focuses on locally linear embedding algorithm into LLE proposed supervised locally linear embedding algorithm (SLLE). That supervised manifold learning algorithm, which introduced adjustable parameters to effectively use the classification information, so as to make the SLLE have a stronger effect for classification problems. Finally, through a series of experiments to fully illustrate the proposed improvement of the effectiveness of the algorithm, the proposed oversight of the manifold learning algorithm can more effectively enhance manifold learning algorithms for classification problems proficiency.

scholarly journals A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

2014 ◽  
Vol 51 (1) ◽  
pp. 57-73 ◽  
Author(s):  
Karol Deręgowski ◽  
Mirosław Krzyśko
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Principal Components ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Kernel Trick ◽  
New Learning ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

SUMMARY Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC

Sign in / Sign up

Export Citation Format

Share Document