Kernel Principal Component Analysis Approach Research Articles

Optimizing lung cancer prediction: leveraging Kernel PCA with dendritic neural models

Lung cancer is considered a cause of increased mortality rate due to delays in diagnostics. There is an urgent need to develop an effective lung cancer prediction model that will help in the early diagnosis of cancer and save patients from unnecessary treatments. The objective of the current paper is to meet the extensiveness measure by using collaborative feature selection and feature extraction methods to enhance the dendritic neural model (DNM) in comparison to traditional machine learning (ML) models with minimum features and boost the accuracy, precision, and sensitivity of lung cancer prediction. Comprehensive experiments on a dataset comprising 1000 lung cancer patients and 23 features obtained from Kaggle. Crucial features are identified, and the proposed method’s effectiveness is evaluated using metrics such as accuracy, precision, F1 score, sensitivity, specificity, and confusion matrix against other ML models. Feature extraction techniques including Principal Component Analysis (PCA), Kernel PCA (K-PCA), and Uniform Manifold Approximation and Projection (UMAP) are employed to optimize model performance. PCA evaluated the DNM accuracy at 96.50%, precision at 96.64% and 97.45% sensitivity. K-PCA explained the DNM accuracy of 98.50%, precision rate of 99.42%, and 98.84% sensitivity and UMAP elaborated the DNM accuracy of 98%, precision of 98.82%, and 98.82% sensitivity. The K-PCA approach showed outstanding performance in enhancing the DNM model. Highlighting the DNM's accurate prediction of lung cancer. These results emphasize the potential of the DNM model to contribute positively to healthcare research by providing better predictive outcomes.

A Self-Tuning KPCA-Based Approach to Fault Detection in Chiller Systems

Several faults affect heating, ventilation, and air conditioning (HVAC) chiller systems, leading to energy wastage, discomfort for the users, shorter equipment life, and system unreliability. Early detection of anomalies can prevent further deterioration of the chiller and energy wastage. In this work, a data-driven approach is used in order to detect faults that usually plague chiller systems. In particular, the proposed approach employs a kernel principal component analysis (KPCA) in order to capture the normal operative conditions of the system; the learning method turns out to be effective in handling nonlinear phenomena through the use of the Gaussian kernel, which, by means of a self-tuning procedure, ensures good accuracy properties while maintaining enough generalization characteristics. The effectiveness of the proposed fault detection method is evaluated by means of tests on emulated and real chiller data. The KPCA approach is first proved to exhibit better detection performances than linear PCA, and then, it is corroborated through the comparison with local outlier factor, one-class support vector machine, and isolation forest.

Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA

In this paper, sensor fault detection and isolation of time-varying nonlinear dynamical systems is studied by utilizing an adaptive kernel principal component analysis (KPCA) solution as a useful method to overcome the weaknesses of conventional KPCA approach in dealing with time-varying dynamical processes. Toward this goal, adaptive Hotelling's T2 is used with KPCA to tackle the time-varying behavior of nonlinear systems. Moreover, for fault isolation, partial adaptive KPCA (AKPCA) is proposed where a set of residual signals is generated based on the structured residual set framework. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear dynamic model of an aeroderivative gas turbine can be effectively detected and isolated in the presence of component degradation.

Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components

We study the estimation of conditional mean regression functions through the so-called subset-based kernel principal component analysis (KPCA). Instead of using one global kernel feature space, we project a target function into different localized kernel feature spaces at different parts of the sample space. Each localized kernel feature space reflects the relationship on a subset between the response and covariates more parsimoniously. When the observations are collected from a strictly stationary and weakly dependent process, the orthonormal eigenfunctions which span the kernel feature space are consistently estimated by implementing an eigenanalysis on the subset-based kernel Gram matrix, and the estimated eigenfunctions are then used to construct the estimation of the mean regression function. Under some regularity conditions, the developed estimator is shown to be uniformly consistent over the subset with a convergence rate faster than those of some well-known nonparametric estimation methods. In addition, we also discuss some generalizations of the KPCA approach, and consider using the same subset-based KPCA approach to estimate the conditional distribution function. The numerical studies including three simulated examples and two real data sets illustrate the reliable performance of the proposed method. In particular, the improvement over the global KPCA method is evident.

Fault detection for turbine engine disk based on an adaptive kernel principal component analysis algorithm

For commercial aircraft, real-time fault detection is essential for condition monitoring of rotating engine components, which can improve aviation safety and reduce maintenance cost for airline companies. In this article, based on the adaptive kernel principal component analysis method, a real-time fault detection algorithm is proposed for turbine engine disk condition monitoring. A sample reduction strategy based on the k-nearest neighbors method is presented to speed up the kernel principal component analysis approach while still guaranteeing correct results. To efficiently detect fault, the fault detection model is updated timely to suit the working process of turbine engine disk. Sample clusters are obtained through the k-mean method, and the parameter of the kernel function is adaptively adjusted by minimizing the within-cluster distance and maximizing the between-cluster distance in the feature space. Experiments have demonstrated the superiority of the proposed approach in fault detection for turbine engine disk.

Fault diagnosis for the landing phase of the aircraft based on an adaptive kernel principal component analysis algorithm

Kernel principal component analysis is an effective fault diagnosis algorithm proved by large amount of practices in industry; however, kernel principal component analysis with constant parameters is unable to achieve satisfactory results when the working condition is dynamic. In this article, the parameters in the proposed kernel function are adaptively adjusted according to the maximum variance principle and the K-nearest neighbors approach. Because the variance of the data is maximized, the proposed adaptive kernel principal component analysis approach can obtain well diagnosis effect under the condition that only a small amount of data are available. Moreover, for the structure of the data is preserved by the K-nearest neighbors method, the rate of false alarm and underreporting is effectively reduced. In addition, a novel approach with a new algorithm for forgetting factor calculation is put forward so that the fault detection model can be updated and suits the actual situation better. Experiments on the landing phase of the aircraft have shown that adopting the proposed algorithm can detect faults better than using static model.

Sensor Fault Detection and Isolation of an Industrial Gas Turbine Using Partial Kernel PCA

In this paper, partial kernel principal component analysis (PKPCA) is studied for sensor fault detection and isolation of an aeroderivative industrial gas turbine. Principal component analysis (PCA) is an effective tool for process monitoring task, however it can achieve acceptable results only for linear processes. In the case of nonlinear processes such as gas turbines, kernel PCA approach can be used which leads to more accurate health monitoring. In order to achieve fault isolation, partial KPCA is proposed where the parity relation concept is used to generate a set of residual signals. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in an industrial gas turbine can be effectively detected and isolated.

A Cellular Automata Model Based on Nonlinear Kernel Principal Component Analysis for Urban Growth Simulation

In this paper we present a cellular automata (CA) model based on nonlinear kernel principal component analysis (KPCA) to simulate the spatiotemporal process of urban growth. As a generalisation of the linear principal component analysis (PCA) method, the KPCA method was developed to extract the nonspatially correlated principal components amongst the various spatial variables which affect urban growth in high-dimensional feature space. Compared with the linear PCA method, the KPCA approach is superior as it generates fewer independent components while still maintaining its capacity to reduce the noise level of the original input datasets. The reduced number of independent components can be used to better reconstruct the nonlinear transition rules of a CA model. In addition, the principal components extracted through the KPCA approach are not linearly related to the input spatial variables, which accords well with the nonlinear nature of complex urban systems. The KPCA-based CA model (KPCA-CA) developed was fitted to a fast-growing region in China's Shanghai Metropolis for the sixteen-year period 1992–2008. The simulated patterns of urban growth matched well with the observed urban growth, as determined from historical remotely sensed images for the same period. The KPCA-CA model resulted in significant improvements in locational accuracy when compared with conventional CA models and acted to reduce simulation uncertainty.

An Improved Face Recognition Using Neighborhood Defined Modular Phase Congruency Based Kernel PCA

A face recognition algorithm based on NMPKPCA algorithm presented in this paper. The proposed algorithm when compared with conventional Principal component analysis (PCA) algorithms has an improved recognition Rate for face images with large variations in illumination, facial expressions. In this technique, first phase congruency features are extracted from the face image so that effects due to illumination variations are avoided by considering phase component of image. Then, face images are divided into small sub images and the kernel PCA approach is applied to each of these sub images. but, dividing into small or large modules creates some problems in recognition. So a special modulation called neighborhood defined modularization approach presented in this paper, so that effects due to facial variations are avoided. Then, kernel PCA has been applied to each module to extract features. So a feature extraction technique for improving recognition accuracy of a visual image based facial recognition system presented in this paper.

An ensemble kernel classifier with immune clonal selection algorithm for automatic discriminant of primary open-angle glaucoma

An ensemble kernel classifier is proposed in this paper by integrating a kernel principal component analysis (KPCA) with a support vector machine (SVM) as well as an immune clonal selection algorithm (ICSA). The KPCA approach is used to extract features, whereas the SVM technique is employed to deal with classification, and the ICSA is applied to optimize the parameters of the proposed scheme. The proposed ensemble classifier can automatically select the kernel type and optimize its parameter sets, in order to produce various SVM classifiers with different kernels. Regardless of whether the data is linear or nonlinear, an optimum classification result can be obtained. In order to demonstrate the effectiveness of the classifier, it is applied to discriminate the primary open-angle glaucoma (POAG) using a standard classification dataset. Experimental results reveal that the proposed ensemble classifier is accurate and more effective when compared to other approaches in the literature. It is envisaged that ensemble kernel classifier could hold a high potential in classification of pattern recognition problems.

Automated Nonlinear Feature Generation and Classification of Foot Pressure Lesions

Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health care challenge, and failure to detect lesions early can have significant effects on patient prognoses. Most of the previous works on plantar lesion identification employed the analysis of biomechanical microenvironment variables like pressure and thermal fields. This paper focuses on foot kinematics and applies kernel principal component analysis (KPCA) for nonlinear dimensionality reduction of features, followed by Fisher's linear discriminant analysis for the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. Performance comparisons are made using leave-one-out cross-validation. Results show that the proposed method can lead to approximately 94% correct classification rates, with a reduction of feature dimensionality from 2100 to 46, without any manual preprocessing or elaborate feature extraction methods. The results imply that foot kinematics contain information that is highly relevant to pathology classification and also that the nonlinear KPCA approach has considerable power in unraveling abstract biomechanical features into a relatively low-dimensional pathology-relevant space.

3-D Head Model Retrieval Using a Single Face View Query

In this paper, a novel 3D head model retrieval approach is proposed, in which only a single 2D face view query is required. The proposed approach will be important for multimedia application areas such as virtual world construction and game design, in which 3D virtual characters with a given set of facial features can be rapidly constructed based on 2D view queries, instead of having to generate each model anew. To achieve this objective, we construct an adaptive mapping through which each 2D view feature vector is associated with its corresponding 3D model feature vector. Given this estimated 3D model feature vector, similarity matching can then be performed in the 3D model feature space. To avoid the explicit specification of the complex relationship between the 2D and 3D feature spaces, a neural network approach is adopted in which the required mapping is implicitly specified through a set of training examples. In addition, for efficient feature representation, principal component analysis (PCA) is adopted to achieve dimensionality reduction for facilitating both the mapping construction and the similarity matching process. Since the linear nature of the original PCA formulation may not be adequate to capture the complex characteristics of 3D models, we also consider the adoption of its nonlinear counterpart, i.e., the so-called kernel PCA approach, in this work. Experimental results show that the proposed approach is capable of successfully retrieving the set of 3D models which are similar in appearance to a given 2D face view.

Process Monitoring of an Electro-Pneumatic Valve Actuator Using Kernel Principal Component Analysis

Abstract In this paper, an approach for process monitoring using a multivariate statistical technique, namely kernel principal component analysis is studied. Kernel principal analysis has recently been proposed as a new method for performing a nonlinear form of principal component analysis (PCA). The basic idea of kernel PCA is to first map the input space into a feature space via a nonlinear map and then compute the principal components in that feature space. For the process monitoring application, reconstructed input patterns can be obtained by approximating the pre-image of scores in feature space. An application study of an electro-pneumatic valve actuator in a sugar factory is described. The results show that the kernel PCA approach can detect several actuator faults earlier than linear PCA This study indicates the great potential of Kernel PCA for process monitoring.