Nonlinear Feature Extraction Method Research Articles

Systematic comparison of unsupervised feature extraction methods for enhancing discrimination accuracy of brown rice flour adulteration in LIBS

Laser-induced breakdown spectroscopy (LIBS) is widely applied in various critical fields due to its technical advantages. However, with the explosive growth in spectral data volume, the challenges of high-dimension and redundant information of spectra are becoming increasingly prominent. Therefore, dimensionality reduction of spectra is crucial for LIBS. In this study, to enhance the discrimination accuracy of brown rice flour and its adulterants, a systematic comparative analysis of eight unsupervised feature extraction methods was conducted, including principal component analysis (PCA), non-negative matrix factorization (NMF), independent component analysis (ICA), multidimensional scaling (MDS), kernel PCA (KPCA), locally linear embedding (LLE), Laplacian eigenmaps (LE), and isometric mapping (ISOMAP). The results indicate that linear feature extraction methods (PCA, NMF, ICA, and MDS) show a more obvious improvement in the analytical performance of the classification models, compared to nonlinear feature extraction methods (KPCA, LLE, LE, and ISOMAP). Among these methods, the commonly used PCA has the best improvement effect. The accuracy, macro precision, macro recall, and macro F1-score of the classification models improve from 92.73%, 94.13%, 92.92%, and 0.93 to 95.52%, 95.67%, 95.63%, and 0.96. Therefore, this study can provide experience and guidance for selecting appropriate unsupervised feature extraction methods in subsequent research, contributing to the further application and development of LIBS.

FDEPCA: A Novel Adaptive Nonlinear Feature Extraction Method via Fruit Fly Olfactory Neural Network for IoMT Anomaly Detection.

With the rapid development of 5G communication technology, the data in the Internet of Medical Things (IoMT) application systems exhibits complex characteristics such as large volume, high dimensionality, nonlinearity, and diversity, which significantly affect the efficiency and detection performance of anomaly detection tasks. How to efficiently extract nonlinear features from high-dimensional data in the context of the IoMT while minimizing information distortion in data objects are challenging problems in recent academic research. A novel adaptive nonlinear feature extraction method via fruit fly olfactory neural network (Fly dimension expansion projection and remain main components by PCA, FDEPCA) is proposed, where 1) the data are mean-centered; 2) a binary sparse random projection matrix is used for dimension expansion projection; and 3) PCA is used to extract principal component information. The proposed method overcomes the problems of present nonlinear feature extraction in the face of high-dimensional outliers where the intrinsic geometric structure of the data is severely distorted and computationally expensive. The dataset after nonlinear feature extraction by the FDEPCA algorithm is applied to specific anomaly detection models, using ROC curves and AUC as evaluation metrics for classification performance. Extensive comparison experiments are conducted on eight publicly available datasets, and experimental results show that compared with the popular nonlinear feature extraction algorithms, the FDEPCA algorithm has better classification performance and projection time advantage. When applied to proximity-based, probability-based, and ensemble-based different anomaly detection models respectively, the FDEPCA algorithm exhibits strong applicability in different types of anomaly detection classifiers.

Inspection of improved ghost imaging for hight-light LED with different factory quality

High brightness and high reflective properties are two important challenges for machine vision inspection. We propose a new method for nonlinear feature extraction and inspection of visual weak signals from highlight-emitting rather than “interference” bright objects. Based on the ghost image principle, the highlight characteristics of the light source become a useful characteristic. Under the guidance of ghost image theory, optimized design of ghost lens group, visual inspection of bright objects for the purpose of obtaining ghost images with high signal-to-noise ratio. We improved the high-brightness LED appearance quality inspection experiment of ghost image. Results show that the inspection method of improved ghost image of highlighted objects combined with deep learning has a good detection result. The method has an accuracy rate of 90.1%. In the early stages of the visual inspection process, there is great potential in applying the theory and method of ghost image to detect objects with high brightness and high reflective properties.

Sludge bulking monitoring in industrial wastewater treatment plants through graphical methods: A dynamic graph embedding and Bayesian networks approach

Sludge bulking is a prevalent issue in wastewater treatment plants (WWTPs) that negatively impacts effluent quality by hindering the normal functioning of treatment processes. To tackle this problem, we propose a novel graph-based monitoring framework that employs advanced graph-based techniques to detect and diagnose sludge bulking events. The proposed framework utilizes historical datasets under normal operating conditions to extract pertinent features and causal relationships between process variables. This enables operators to trigger alarms and diagnose the root cause of the bulking event. Sludge bulking detection is carried out using the dynamic graph embedding (DGE) method, which identifies similarities among process variables in both temporal and neighborhood dependencies. Consequently, the dynamic Bayesian network (DBN) computes the prior and posterior probabilities of a belief, updated at each time step. Variations in these probabilities indicate the potential root cause of the sludge bulking event. The results demonstrate that the DGE outperforms other linear and non-linear feature extraction methods, achieving a detection rate of 99%, zero false alarms, and less than one percent incorrect detections. Additionally, the DBN-based diagnostic method accurately identified the majority of sludge bulking root causes, primarily those resulting from sudden drops in COD concentration, with an accuracy of 98% an improvement of 11% over state-of-the-art techniques.

Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition

Emotional recognition through exploring the electroencephalography (EEG) characteristics has been widely performed in recent studies. Nonlinear analysis and feature extraction methods for understanding the complex dynamical phenomena are associated with the EEG patterns of different emotions. The phase space reconstruction is a typical nonlinear technique to reveal the dynamics of the brain neural system. Recently, the topological data analysis (TDA) scheme has been used to explore the properties of space, which provides a powerful tool to think over the phase space. In this work, we proposed a topological EEG nonlinear dynamics analysis approach using the phase space reconstruction (PSR) technique to convert EEG time series into phase space, and the persistent homology tool explores the topological properties of the phase space. We perform the topological analysis of EEG signals in different rhythm bands to build emotion feature vectors, which shows high distinguishing ability. We evaluate the approach with two well-known benchmark datasets, the DEAP and DREAMER datasets. The recognition results achieved accuracies of 99.37% and 99.35% in arousal and valence classification tasks with DEAP, and 99.96%, 99.93%, and 99.95% in arousal, valence, and dominance classifications tasks with DREAMER, respectively. The performances are supposed to be outperformed current state-of-art approaches in DREAMER (improved by 1% to 10% depends on temporal length), while comparable to other related works evaluated in DEAP. The proposed work is the first investigation in the emotion recognition oriented EEG topological feature analysis, which brought a novel insight into the brain neural system nonlinear dynamics analysis and feature extraction.

Nonlinear Feature Extraction Methods Based on Dual-Tree Complex Wavelet Transform Subimages of Brain Magnetic Resonance Imaging for the Classification of Multiple Diseases.

It has been a long time since we use magnetic resonance imaging (MRI) to detect brain diseases and many useful techniques have been developed for this task. However, there is still a potential for further improvement of classification of brain diseases in order to be sure of the results. In this research we presented, for the first time, a non-linear feature extraction method from the MRI sub-images that are obtained from the three levels of the two-dimensional Dual tree complex wavelet transform (2D DT-CWT) in order to classify multiple brain disease. After extracting the non-linear features from the sub-images, we used the spectral regression discriminant analysis (SRDA) algorithm to reduce the classifying features. Instead of using the deep neural networks that are computationally expensive, we proposed the Hybrid RBF network that uses the k-means and recursive least squares (RLS) algorithm simultaneously in its structure for classification. To evaluate the performance of RBF networks with hybrid learning algorithms, we classify nine brain diseases based on MRI processing using these networks, and compare the results with the previously presented classifiers including, supporting vector machines (SVM) and K-nearest neighbour (KNN). Comprehensive comparisons are made with the recently proposed cases by extracting various types and numbers of features. Our aim in this paper is to reduce the complexity and improve the classifying results with the hybrid RBF classifier and the results showed 100 percent classification accuracy in both the two class and the multiple classification of brain diseases in 8 and 10 classes. In this paper, we provided a low computational and precise method for brain MRI disease classification. the results show that the proposed method is not only accurate but also computationally reasonable.

Dense Nested Network Based on Position-Aware Dynamic Parameter Convolution Kernel for Infrared Small Target Detection

Infrared small target detection is widely used in military and civil security fields. Most of the existing infrared small target detection algorithms focus on improving detection accuracy. However, the lightweight and generalization capabilities of the network have not improved significantly. To improve the network's generalization ability and lightweight while main- taining accuracy, a dense nested network based on position aware dynamic parameter convolution kernel (Par-DPC DNNet) was proposed. Firstly, a generation module for the dynamic parameter convolution kernel was proposed. The convolution parameters are generated dynamically based on the instance. This can realize the network's local dynamic change for unfixed small targets and effectively improve the network's generalization ability. Moreover, this can achieve good detection performance on the basis of network depth reduction. Secondly, in order to apply the dynamic parameter convolution kernel in the network on a large scale, inspired by the patch fragment mode in the VIT structure, a new downsampling method was proposed. The dynamic multiples of downsampling are realized by the feature map fragment combined with the convolution method without information loss. Finally, the dynamic parameter convolution kernel and new downsampling were integrated into the dense nested network, and a new nonlinear feature extraction method was adopted. It improves network accuracy while maintaining network generalization and structure optimization. The effective- ness of the proposed method was verified using two data sets. The experimental results demonstrate that the detection performance of Par-DPC DNNet is superior to the existing state-of-the-art methods even if the number of internal layers of resblock is reduced to two.

Reduced order modeling of fluid flows using convolutional neural networks

Application of machine learning is currently one of the hottest topics in the fluid mechanics field. While machine learning seems to have a great possibility, its limitations should also be clarified. In our research group, we have started a research project to construct a nonlinear feature extraction method by applying machine learning techniques to big data of fluid flow, i.e., extracting the low-dimensional nonlinear modes essential to the unsteady flow phenomena and deriving the governing equations for such low-dimensionalized dynamics. This self-review article is a focused but extended version of the keynote lecture given by the author at the 7th International Conference on Jets, Wakes and Separated Flows (ICJWSF2022). We will first introduce the use of a convolutional neural network (CNN) to learn the temporal evolution of cross-sectional velocity field in a turbulent channel flow. Subsequently, we also consider an application of CNN for extraction of low-dimensional dynamics for flow around a bluff body accompanying vortex shedding and our preliminary attempt to use the extracted low-dimensional dynamics for an advanced design of flow control.

EFFECTIVE APPROACH FOR FACE RECOGNITION AND ACTIVE SHAPE 3D MODELS USING KERNEL PRINCIPAL COMPONENT ANALYSIS

Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and surveillance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step for classification algorithm. A face recognition approach based on KPCA and genetic algorithms (GAs) is proposed. By the use of the polynomial functions as a kernel function in KPCA, the high order relationships can be utilized and the nonlinear principal components can be obtained. After that nonlinear principal components, we use GAs to select the optimal feature set for classification.

EEG Sinyallerinden Çıkarılan Poincare Ölçümlerini Kullanarak Çok Sınıflı Motor Hayali Görevlerin Sınıflandırılması

Motor Imaginary (MI) electroencephalography (EEG) signals are generated with the recording of brain activities when a participant imagines a movement without physically performing it. The correct decoding of MI signals have been became an important task due to the application of these signals in the rehabilitation process of paralyzed patients in recent studies. However, the decoding of the these signals is still an evolving challenge in the design of a brain-computer interface (BCI) system. In this study, a machine learning based approach using Poincare measurements from non-linear measurements of MI EEG signals is proposed for classification of four-class MI tasks. The m-lagged Poincare plots were used to extract non-linear features and m is set to be values from 1 to 10. The performances of feature vectors which are extracted from 10 lag values and feature vector which is the combinations of these vectors were investigated separately in experimental evaluation section. The 24 different typical classification algorithms were tested in differentiating MI tasks using 5-fold cross-validation. Each of the these algorithms tested 10 times to analyzed the repeatability of the experimental results. The highest classifier performance of 47.08% among these 11 feature vectors was achieved over the combination feature vector that includes all lag values features using Quadratic Support Vector Machine (SVM). According to average accuracy value of 24 classifiers in 11 feature vector, the most discriminative feature set is 9th vector that consists of features extracted when lag value defined as 9. As a result, the innovative aspect of this study is the application of Poincare plots, one of the nonlinear feature extraction methods, in motor imaginary task classification.

Application of the combination method based on RF and LE in near infrared spectral modeling

The dimensionality of near-infrared (NIR) spectral data is often extremely large. Dimensionality reduction of spectral data can effectively reduce the redundant information and correlation between spectral variables and simplify the model, which is crucial to increasing the model's performance. As a nonlinear feature extraction method, Laplacian Eigenmaps (LE) may preserve the local neighborhood information of the dataset, has high robustness, and is simple to compute. However, when the LE algorithm maps the data from high-dimensional space to low-dimensional space, it is often disturbed by irrelevant information and multicollinearity in the spectral data, which lowers the model's prediction performance. Random Frog (RF) algorithm can eliminate noise and collinearity in the spectrum. Therefore, before using the LE algorithm, we use the RF algorithm to eliminate irrelevant information in the spectrum and reduce the correlation between the spectra variables to increase the efficiency of the LE algorithm. We used the RF + LE algorithm to reduce the dimensionality of two public NIRS datasets (soil datasets and pharmaceutical tablets datasets) and compared it with RF and LE algorithms alone. We utilized Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) to establish regression models. The experimental findings demonstrate that compared with the RF algorithm and LE algorithm, the RF + LE combination method can reduce the dimension of spectral variables and model complexity, and improve regression models' prediction accuracy and stability. It is an effective dimensionality reduction method for the near-infrared spectrum.

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study.

Heart failure (HF) is the main manifestation of cardiovascular disease. Recent studies have shown that various RNA molecules and their complex connections play an essential role in HF’s pathogenesis and pathological progression. This paper aims to mine key RNA molecules associated with HF. We proposed a Prior-knowledge Driven Joint Deep Semi-Negative Matrix Factorization (PD-JDSNMF) model that uses a hierarchical nonlinear feature extraction method that integrates three types of data: mRNA, lncRNA, and miRNA. The PPI information is added to the model as prior knowledge, and the Laplacian constraint is used to help the model resist the noise in the genetic data. We used the PD-JDSNMF algorithm to identify significant co-expression modules. The elements in the module are then subjected to bioinformatics analysis and algorithm performance analysis. The results show that the PD-JDSNMF algorithm can robustly select biomarkers associated with HF. Finally, we built a heart failure diagnostic model based on multiple classifiers and using the Top 13 genes in the significant module, the AUC of the internal test set was up to 0.8714, and the AUC of the external validation set was up to 0.8329, which further confirmed the effectiveness of the PD-JDSNMF algorithm.

An efficient network behavior anomaly detection using a hybrid DBN-LSTM network

The Internet environment is exposed to diverse and increasingly numerous intrusion attacks due to its continuously expanding scale, threatening the information and assets of individuals and companies. The application of machine learning and deep learning methods has significantly improved the performance of network behavior anomaly detection (NBAD). However, existing NBAD methods based on machine learning classify network behaviors with hand-selected feature vectors, which are not flexible enough to adapt to various cyber environments and new categories of attacks, resulting in low accuracy. Moreover, high-dimensional and large-scale data have significantly increased the training, retraining, and detection time, resulting in low scalability. To solve these problems, an efficient NBAD algorithm based on deep belief networks (DBN) and long short-term memory (LSTM) networks is proposed. First, a nonlinear feature extraction method using a DBN is applied to extract features automatically and reduce the dimension of the original data while guaranteeing accuracy. Then, a light-structure LSTM network is used to obtain the classification results. The results of multiple experiments show that the proposed approach performs well in feature learning and has high accuracy while obtaining results in a timely manner and easily updating the model.

Effects of hybrid non-linear feature extraction method on different data sampling techniques for liver disease prediction

Liver disease indicates inflammatory condition of the liver, liver cirrhosis, cancer, or an overload of toxic substances. A liver transplant may reinstate and extend life if a patient has severe liver disease. In the last few years, machine learning (ML) based diagnosis systems have played a vital role in assessing liver patients which eventually leads to proper treatment and saves human life. In this study, we try to predict liver patients by adopting a hybrid feature extraction method to enhance the performance of the ML algorithm. Medical data frequently exhibits non-linear patterns and class imbalances. This is undesirable for the majority of ML algorithms and degrades performance. Here, we present a hybrid feature space that combines t-SNE, Isomap nonlinear features, and kernel principal components that can explain 90% of the variation in the data as a solution to this issue. Before feeding the ML model, data preprocessing techniques including class balancing, identifying outliers, and impute missing values are used. A simulation study and ensemble learning also conducted to justify the proposed prediction performances. Our suggested hybrid non-linear feature exhibits a 2-20 % improvement over existing studies and the ensemble classifier achieved an ideal and outstanding accuracy of 91.33 %.

Commodity Price Recognition and Simulation of Image Recognition Technology Based on the Nonlinear Dimensionality Reduction Method

Dimensionality reduction of images with high-dimensional nonlinear structure is the key to improving the recognition rate. Although some traditional algorithms have achieved some results in the process of dimensionality reduction, they also expose their respective defects. In order to achieve the ideal effect of high-dimensional nonlinear image recognition, based on the analysis of the traditional dimensionality reduction algorithm and refining its advantages, an image recognition technology based on the nonlinear dimensionality reduction method is proposed. As an effective nonlinear feature extraction method, the nonlinear dimensionality reduction method can find the nonlinear structure of datasets and maintain the intrinsic structure of data. Applying the nonlinear dimensionality reduction method to image recognition is to divide the input image into blocks, take it as a dataset in high-dimensional space, reduce the dimension of its structure, and obtain the low-dimensional expression vector of its eigenstructure so that the problem of image recognition can be carried out in a lower dimension. Thus, the computational complexity can be reduced, the recognition accuracy can be improved, and it is convenient for further processing such as image recognition and search. The defects of traditional algorithms are solved, and the commodity price recognition and simulation experiments are carried out, which verifies the feasibility of image recognition technology based on the nonlinear dimensionality reduction method in commodity price recognition.

Application of Coupled LDA–KPCA and BO–MKRVM Model to Predict Coal and Gas Outbursts

This paper proposed a coupled model of effective feature extraction and optimized classifier, which can overcome the existing problems of coal and gas outbursts classification in the literatures. Firstly, we support the use of kernel principal component analysis and linear discriminant analysis methods to extract linear and nonlinear feature information from coal and gas outbursts influencing factors. Secondly, in order to realize the complementarity and correlation between coal and gas outbursts influencing factors, we perform parallel feature fusion to combine the extracted linear and nonlinear feature information. Thirdly, an improved classifier called BO–MKRVM has been proposed that combines mixed kernel relevance vector machine (MKRVM) and Bayesian optimization (BO) algorithm to predict coal and gas outbursts according to the extracted features. In BO–MKRVM model, an effective mixed kernel function which combines Gaussian kernel function and Sigmoid kernel function is proposed to improve the learning and generalization ability of the MKRVM classifier, then the BO and tenfold cross-validation are utilized to optimize kernel parameters and weight coefficients of MKRVM with strong global and local search capability, the proposed BO–MKRVM classifier is performed on coal and gas outbursts dataset. Compared with the single feature extraction method, the combination of linear and non-linear feature extraction methods can obtain complete feature information and contribute to the classification performance of outbursts. The mixed kernel function considers the characteristics of coal and gas outbursts sample data, which can also effectively improve the outbursts accuracy. After the MKRVM classifier is optimized by BO algorithm, the BO–MKRVM classifier has better fitting effect and generalization ability, and obtains higher accuracy with a lower time. The experimental results are obviously better than those of other classification models, which further verifies the applicability of the proposed coupled model.

Analysis of the Information Transfer Rate-ITR in Linear and Non-linear Feature Extraction Methods for SSVEP Signals

The most popular paradigm in BCIs is the steady-state visually evoked potential (SSVEP) due to their advantages, such as the high information transfer rate (ITR), the time spent on users in the training phase, and the capacity to discriminate each stimulus. One of the most influential factors in the ITR evaluation is the feature extraction methods since these can increase the accuracy. Here, we compare nine methods for the extraction of features from SSVEP signals to identify those with better performance, according to the time window (TW), its technology (equipment and number of nodes), and the value of ITR. The study identifies two groups: the first one is characterized by presenting variations of correlated component analysis (CCA), which is highly used to increase the ITR due to its efficiency in classification and its capacity of response to reduction (TW), such as MsetCCA, IT-CCA, FBCCA; the second one are the representation special based methods that consider the non-linear nature of the electroencephalogram (EEG) signal such as TRCA, CORRCA, EMD, and VMD. The results show a considerable difference between these groups. The maximum ITR value for FBCCA was 117.75 [bits/min] in a TW of 1.25s, while the VMD method achieved 3120 [bits/min] in a TW of 1s, respectively. The comparison covers signals between 0.55 and 8 seconds, taking into account visual strain, the experimental environment, and other artifacts.

Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude‐Aware Permutation Entropy and Pairwise Feature Proximity

With a view to solving the defect that multiscale amplitude‐aware permutation entropy (MAAPE) can only quantify the low‐frequency features of time series and ignore the high‐frequency features which are equally important, a novel nonlinear time series feature extraction method, hierarchical amplitude‐aware permutation entropy (HAAPE), is proposed. By constructing high and low‐frequency operators, this method can extract the features of different frequency bands of time series simultaneously, so as to avoid the issue of information loss. In view of its advantages, HAAPE is introduced into the field of fault diagnosis to extract fault features from vibration signals of rotating machinery. Combined with the pairwise feature proximity (PWFP) feature selection method and gray wolf algorithm optimization support vector machine (GWO‐SVM), a new intelligent fault diagnosis method for rotating machinery is proposed. In our method, firstly, HAPPE is adopted to extract the original high and low‐frequency fault features of rotating machinery. After that, PWFP is used to sort the original features, and the important features are filtered to obtain low‐dimensional sensitive feature vectors. Finally, the sensitive feature vectors are input into GWO‐SVM for training and testing, so as to realize the fault identification of rotating machinery. The performance of the proposed method is verified using two data sets of bearing and gearbox. The results show that the proposed method enjoys obvious advantages over the existing methods, and the identification accuracy reaches 100%.

A new stable nonlinear textural feature extraction method based EEG signal classification method using substitution Box of the Hamsi hash function: Hamsi pattern

BackgroundThe EEG signal classification is crucial for epileptic seizure prediction. Therefore, many machine learning model has been presented to classify EEG signals accurately. Material and MethodThis work presents a novel automated EEG classification method by using a novel nonlinear feature extractor, and it is called as Hamsi-Pat. It uses the substitution box (S-Box) of the Hamsi hash function. As stated in the literature, S-Boxes have generally used for diffusion in symmetric encryption (especially block ciphers) methods and cryptologic hash functions. Since it is a nonlinear structure, this work aims to illustrate the merit of an S-Box for feature generation. Therefore, a new generation feature generator, which is Hamsi-Pat, is presented by using S-Box of the Hamsi hash function, and a novel EEG classification method is proposed by using Hamsi-Pat. The presented biomedical signal classification method has three elementary phases, and these phases are Hamsi-Pat based multileveled feature generation, iterative neighborhood component analysis (INCA) selector based feature dimension reduction, and classification using k nearest neighborhood (kNN) classifier. The presented Hamsi-Pat and INCA based methods were tested on Bonn electroencephalography (EEG) datasets. ResultThis model yielded 99.20% classification accuracy on the used EEG dataset for five classes case and it yielded 100.0% accuracies for other cases. ConclusionThese results obviously denoted that the S-Boxes can be considered as a feature generator, and a novel S-Box based feature generation research area can be defined as textural feature generation and statistical feature generation.

Fast recognition and classification of static and dynamic signs for Persian sign language

Sign Language (SL) is the most effective way for communication between deaf and hearing-impaired people. Since most non-deaf people are not familiar with SL, a vision-based translator/interpreter can be a very useful tool to enhance their communication. This paper presents a recognition system for Persian static and dynamic signs. The system is designed based on proposed modified non-linear kernel-based fast feature extraction methods, consisting of hybrid kernel principal component analysis and hybrid kernel discriminant analysis. For recognition of dynamic signs, the proposed feature extraction method is employed in association with spatio-temporal approach. The proposed methods are examined and compared with several existing feature extraction methods, including linear and non-linear kernel-based methods. The experiments indicate that our feature extraction methods significantly outperform other methods and reduce computational time while they achieve high recognition rates. Our simulations achieved a promising classification accuracy rate of 96.78% on static and 96.99% on dynamic signs, respectively.