Face Recognition with Kernel Principal Component Analysis and Support Vector Machine

Automated classification of magnetic resonance brain images (MRIs) is a hot topic in the field of medical and biomedical imaging. Various methods have been suggested recently to improve this technology. In this paper, to reduce the complexity involved in the medical images and to ameliorate the classification of MRIs, a novel 3D magnetic resonance (MR) brain image classifier using kernel principal component analysis (KPCA) and support vector machines (SVMs) is proposed. Experiments are carried out using A deep multiple kernel SVM (DMK-SVM) and a regular SVM. An algorithm entitled SVM–KPCA is put forward. Its main task is to classify a brain MRI as a normal brain image or as a pathological brain image. This algorithm, firstly, adopts the discrete wavelet transform technique to extract features from images. Secondly, KPCA is applied to decrease the dimensionality of features. SVM is then applied to the reduced data. A K-fold cross-validation strategy is used to avoid overfitting and to ameliorate the generalization of the SVM–KPCA algorithm. Three databases are used to validate the suggested SVM–KPCA method. Three conclusions are obtained from this work. First, KPCA is highly efficient in increasing the classifier’s performance compared with similar algorithms working on the proposed database. Second, the SVM–KPCA algorithm performs well in differentiating between two classes of medical images. Third, the approach is robust and might be utilized for other MRIs. This proposes a significant role for computer aided diagnosis analysis systems used for clinical practice.

Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange

In this paper, a novel HVAC fan machinery fault recognition method combining kernel principal component analysis (KPCA) and support vector machine (SVM) is proposed. KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Support vector machine (SVM) is a novel approach based on statistical learning theory, which has emerged for feature identification and classification. An integrated method is applied for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. The experimental results show that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed.

Focusing on feature extraction of non-stationary vibration signals in condition monitoring and fault diagnosis of gear pump, the fault diagnosis approach based on empirical mode decomposition method and kernel principal component analysis (KPCA) and support vector machines (SVM) is proposed. The improved empirical mode decomposition (EMD) method is used to decompose the mechanical equipment output signal into a number of intrinsic mode function (IMF) components and a residue component, and calculated ten dimensionless parameters of each IMF and residue component, then extract result from the original parameters by using KPCA, at last the kernel principal component is classified by inputting the new feature vector to SVM for training and recognizing. The simulation and experiment results show that the advanced method is effective in restraining end effect, and the analysis result of gear pump vibration signals in different conditions validate the method is effective.

This study developed a driving mental fatigue estimation system based on electroencephalogram (EEG) when he drives a car in a virtual reality (VR)-based dynamic simulator. To classify driver's mental fatigue status, the features of multichannel electroencephalographic (EEG) signals of frontal, central and occipital are extracted by multivariate autoregressive (MVAR) model. Then kernel principal component analysis (KPCA) and support vector machines (SVM) are combined to identify three-levels driving mental fatigue. The results show that KPCA is an good feature extractor which can effectively reduce the dimensionality of the feature vectors. The KPCA-SVM shows good performance with higher classification accuracy (81.64%) across 10 subjects. This method could be an potential approach of classification of driving mental fatigue.

Support Vector Machines and Kernel methods have become very popular as methods for learning from examples. In this paper, in order to solve the non-linear and the time of recognition problem, recent advanced techniques are presented. For example, Kernel Principal Components and Proximal Support Vector Machines, Kernel Principal Components Analysis can describe multiple correlations between pixels in the image recognition, and can handle the high order statistics of the original data, at the same time the nonlinear features of the image can be preferably extracted. Proximal Support Vector Machines is an improvement for these characters of Support Vector Machines, combines the proximal algorithm with Support Vector Machines, has a better ability of nonlinear mapping and a stronger generalization capability, and the pace of Proximal Support Vector Machines is very enhanced. In this paper, the computer simulation was progressed based on ORL face database, the effectiveness of the Proximal Support Vector Machines algorithm and Kernel Principal Components Analysis algorithm were shown by experimental results.

Application of KPCA combined with SVM in Raman spectral discrimination

Muscle fatigue is an inevitable phenomenon in athletic training, and its accurate assessment is crucial for preventing injuries. To address the issues of high redundancy in multi-channel surface electromyography (sEMG) features and the limitations of linear dimensionality reduction methods, this study proposes a muscle fatigue recognition model based on Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM). sEMG signals were collected from 12 subjects performing sustained contractions. A comprehensive set of 48 time-domain, frequency-domain, and nonlinear features was extracted. KPCA was employed for nonlinear dimensionality reduction before classification. The resulting features were fed into an SVM classifier to distinguish between three fatigue states: relaxed, transitional fatigue, and fatigued. The model was evaluated using 10-fold cross-validation. Results demonstrated that the KPCA-SVM combination achieved the highest performance, with an average recognition accuracy of 91.5%, precision of 0.91, recall of 0.93, and F1-score of 0.92, outperforming other combinations of dimensionality reduction methods (MI, PCA) and classifiers (FLDA, KNN). This method provides an effective tool for the objective assessment of muscle fatigue in athletes.

Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

Time series data are collected based on certain periods which have constants value (e.g. daily, weekly or monthly), it can be used to forecast or predict future circumstance. Prediction is one of the objectives of the time series analysis by identifying the model from previous data and assuming the current information will also occur in the future. In Big Data trend, huge amount of time series data come from different heterogeneous sources and multiple application domains. This present a new challenge for time series forecasting. Time series modelling has been widely applied and proposed in various fields to improve its accuracy and efficiency of forecasting. This paper discusses time series forecasting for electricity consumption using kernel principal component analysis (KPCA) and Support Vector Machine (SVM). This research will be measured with Mean Absolute Error and Mean Squared Error.

In kernel methods such as kernel principal component analysis (PCA) and support vector machines, the so called kernel trick is used to avoid direct calculations in a high (virtually infinite) dimensional kernel space. In this brief, based on the fact that the effective dimensionality of a kernel space is less than the number of training samples, we propose an alternative to the kernel trick that explicitly maps the input data into a reduced dimensional kernel space. This is easily obtained by the eigenvalue decomposition of the kernel matrix. The proposed method is named as the nonlinear projection trick in contrast to the kernel trick. With this technique, the applicability of the kernel methods is widened to arbitrary algorithms that do not use the dot product. The equivalence between the kernel trick and the nonlinear projection trick is shown for several conventional kernel methods. In addition, we extend PCA-L1, which uses L1-norm instead of L2-norm (or dot product), into a kernel version and show the effectiveness of the proposed approach.

Introduction Intrinsically disordered proteins occur when the deformations happen in the tertiary structure of a protein. Disordered proteins play an important role in DNA/RNA/protein recognition, modulation of specificity/affinity of protein binding, molecular threading, activation by cleavage. The aim of the study is the identification of ordered-disordered protein which is a very challenging problem in bioinformatics. Methods In this paper, this kind of proteins is classified by using linear and kernel (nonlinear) support vector machines (SVM). Results Overall accuracy rate of linear SVM and kernel SVM in identifying the ordered-disordered proteins are 86.54% and 94.23%, respectively. Discussion and conclusion Since kernel SVM gives the best discriminating scheme, it can be referred that it is a very satisfying method to identify ordered-disordered structures of proteins.

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 we obtain the nonlinear principal components, we use GAs to select the optimal feature set for classification. At the recognition stage, we employed linear support vector machines (SVM) as classifier for the recognition tasks. Two face databases were used to test our algorithm and higher recognition rates were obtained which show that our algorithm is effective.

A well-known problematic issue for the gazetteer services that many geospatial applications depend on is the lack of spatial footprints of imprecise regions. We present an automated method of footprint generation based on the statistical evaluation of a set of points, which are assumed to lie in the region. Two statistical methods, Kernel Density Estimation and Support Vector Machines (SVMs), are applied and compared for this task. The overall approach is evaluated using precise regions, and the results obtained from the two classes of methods are evaluated by means of statistical classification measures showing a slight superiority of SVMs. Finally, a priori choices for the input parameters of the methods are derived from the results and footprints of imprecise regions are generated in a completely automated process. The input dataset is acquired from georeferenced photographs freely available on the web.

This study investigates stock market portfolio construction that is an interesting and important research in the areas of investment and applications, as it allows more profits and returns at lower risk rate with effective portfolios. To construct accurate prediction and thus good portfolio construction, various methods have been tried, among which the machine learning methods have drawn attention and been developed. This paper introduces a machine learning method of Support Vector Machine (SVM) to construct a stocks portfolio by selecting well-performing firms in Casablanca Stock Exchange (Casablanca S.E). This model can perform a nonlinear classification. However, the accuracy of SVM classification is particularly sensitive to the quality of data. To insure a better performance of the model, we bring the Kernel Principal Component Analysis (KPCA) into SVM to extract the low-dimensional and efficient feature information. As empirical results show, based on SVM, within KPCA, the stock selection model outperforms the MASI index.