[PDF] Top 20 Kernel Hebbian algorithm for iterative kernel principal component analysis
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Kernel Hebbian algorithm for iterative kernel principal component analysis
... the kernel principal components of a large image ...the kernel matrix the size of which is the square of the number of examples, and necessitates the ... See full document
13
Fast Iterative Kernel Principal Component Analysis
... the kernel Hebbian algorithm (KHA) for iterative kernel PCA (Kim et ...reproducing kernel Hilbert space to further speed up ...on kernel PCA and spectral clustering of ... See full document
26
Kernel Mean Shrinkage Estimators
... reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component ... See full document
41
Batch-to-Batch Iterative Learning Control for End-Point Qualities Based on Kernel Principal Component Regression Model
... model-based iterative learning control (ILC) strategy for the end-point product quality control in batch processes is proposed in this ...using kernel principal component regression ...ILC ... See full document
7
The Attribute Optimization Method Based on the Probability Kernel Principal Component Analysis and Its Application
... The above method is applied to real data to verify the advantages of PKPCA algorithm. The actual data is from Southern oil fields in China. Reservoirs in this block are poor or tight reservoirs, but they have good ... See full document
6
Performance Comparison of Various Kernels of Support Vector Regression for Predicting Option Price
... Firstly, Principal Component Analysis (PCA) is used in order to identify the most influential inputs in the framework of the option pricing model and to reduce the dimensionality of our working ... See full document
11
Power Price Forecasting In the Smart Grid Using Differential Evolution Based SVM Classifier
... a Kernel Principal component analysis (KPCA) and a Differential Evolution based Support vector Machine ...Correlation Analysis combining Random Forest (RF) and Relief-F ...and ... See full document
7
Application of LSSVM to logistics demand forecasting based on grey relational analysis and kernel principal component analysis
... To demonstrate the performance of the proposed model (GRA-KPCA-LSSVM), this paper compares it with the other three models, including GRA-LSSVM, KPCA-LSSVM and LSSVM. For the GRA-LSSVM, the twelve important influential ... See full document
6
A Survey On Face Recognition Systems- Methods And Analysis
... square algorithm to solve the solution of linear regression ...method, kernel linear regression classification (KLRC), based on LRC and the kernel ...high-dimensional kernel space by using the ... See full document
6
MRI BRAIN IMAGE CLASSIFICATION USING POLYNOMIAL KERNEL PRINCIPAL COMPONENT ANALYSIS WITH NEURAL NETWORK
... training algorithm is not suitable because the output of hidden layer is not available for calculating the output and updating the weight so for multilayer perceptions, perception learning algorithm is not ... See full document
8
Quantification and recognition of parkinsonian gait from monocular video imaging using kernel-based principal component analysis
... cipal component analyses (PCA) [20,24]-based feature ...data analysis were implemented using MATLAB (MathWorks, Natick, MA) on a windows XP personal computer equipped with an Intel core 2 duo 6600 processor ... See full document
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A New Approach for Iris and Fingerprint Recognition based on KPCA and LLE Algorithm
... on Kernel Principal Component Analysis (KPCA) and Locally Linear Embedding (LLE) algorithms are proposed for the iris and fingerprint recognition ...non-linear algorithm called Locally ... See full document
5
Title: Evolving Neural Network for Kernel Principal Component Analysis
... D. Hebbian self-learning normalized rule [4] and calculate estimate of w k 1 ( ) eigenvector of the correlation matrix R k ( ) in the learning process, that matches its current maximum eigenvalue 1 ( ) ... See full document
8
Kernel Eigenfaces Framework for Feature Extraction and Face Recognition
... describes principal component analysis (PCA), and linear discriminant analysis (LDA) method, and their algorithms whereas section III describes kernel principal component ... See full document
6
Kernel Hebbian algorithm for single frame super resolution
... from Kernel Principal Component Analysis ...new iterative method for performing KPCA, the Kernel Hebbian ...Generalized Hebbian Al- gorithm, one can iteratively ... See full document
15
Detecting abnormality in optic nerve head images using a feature extraction analysis
... The most widely used diagnostic tool on the HRT is the Moorfields Regression Analysis (MRA) [6]. By comparing the measured neuroretinal rim area to normative limits, globally and in six separate sectors (Fig. 1), ... See full document
17
The Prediction Model of Financial Crisis Based on the Combination of Principle Component Analysis and Support Vector Machine
... Networks Algorithm (NNA) into financial crisis prediction, and selected five Altman financial crisis, and proved that the discrimination accuracy of NNA was higher than one of ...the algorithm runs, it ... See full document
9
Facial Landmark Detection and Localisation using Explicit Model Based Approach
... Facial landmarking techniques are classified as texture based methods and template based methods [1]. Texture based methods may be transform based or template based. Existing work on transform based landmarking ... See full document
8
Iterative Kernel PCA based Classification of Retinal Images for Diabetic Macular Edema
... work, iterative kernel based PCA is proposed which is a novel method used for the classification purpose in diseased retinal ...of iterative kernel based PCA in terms of performance ... See full document
5
Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas
... A nonlinear version of the PCA has been shown to be capable of capturing a part of higher-order statistics, thus better representing the information from the original data set [19, 20]. The first objective of this ... See full document
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