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

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.

ALGORITHM FOR THE DETECTION OF CONGESTIVE HEART FAILURE INDEX

2017 ◽  
Vol 17 (07) ◽  
pp. 1740043 ◽  
Author(s):  
YI DA KANG ◽  
DEMING ZHUO ◽  
RUI EN ANNE FOO ◽  
CHOO MIN LIM ◽  
OLIVER FAUST ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Computer Support ◽  
Electrocardiogram Signals ◽  
Nonlinear Features ◽  
Index Value ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

This study documents our efforts to provide computer support for the diagnosis of congestive heart failure (CHF). That computer support takes the form of an index value. A high index value indicates a low probability of CHF, and an index value below a threshold of 25.6 suggests a high probability of CHF. To create that index, we have designed a sophisticated algorithm chain which takes electrocardiogram signals as input. The signals are pre-processed before they are sent to a range of nonlinear feature extraction algorithms. The top 10 feature extraction methods were used to create the CHF index. By using objective feature extraction algorithms, we avoid the problem of inter- and intra-observer variability. We observed that the nonlinear feature extraction methods reflect the nature of the human heart very well. That observation is based on the fact that the nonlinear features achieved low [Formula: see text]-values and high feature ranking criterion scores.

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.

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