Written for application scientists and graduate students, Kernel-Based Approximation Methods Using MATLAB presents modern theoretical results on kernel-based approximation methods and demonstrates their implementation in various settings. The authors explore the historical context of this fascinating topic and explain recent advances as strategies to address long-standing problems. Examples are drawn from fields as diverse as function approximation, spatial statistics, boundary value problems, machine learning, surrogate modeling, and finance.
kernel-based approximation methods using matlab pdf download
In this work we extend some ideas about greedy algorithms, which are well-established tools for, e.g., kernel bases, and exponential-polynomial splines whose main drawback consists in possible overfitting and consequent oscillations of the approximant. To partially overcome this issue, we develop some results on theoretically optimal interpolation points. Moreover, we introduce two algorithms which perform an adaptive selection of the spline interpolation points based on the minimization either of the sample residuals (f-greedy), or of an upper bound for the approximation error based on the spline Lebesgue function (\(\lambda\)-greedy). Both methods allow us to obtain an adaptive selection of the sampling points, i.e., the spline nodes. While the f-greedy selection is tailored to one specific target function, the \(\lambda\)-greedy algorithm enables us to define target-data-independent interpolation nodes.
Kernel-based classification and regression methods have been successfully applied to modelling a wide variety of biological data. The Kernel-based Orthogonal Projections to Latent Structures (K-OPLS) method offers unique properties facilitating separate modelling of predictive variation and structured noise in the feature space. While providing prediction results similar to other kernel-based methods, K-OPLS features enhanced interpretational capabilities; allowing detection of unanticipated systematic variation in the data such as instrumental drift, batch variability or unexpected biological variation.
The Kernel-OPLS method [21] is a recent reformulation of the original OPLS method to its kernel equivalent. K-OPLS has been developed with the aim of combining the strengths of kernel-based methods to model non-linear structures in the data while maintaining the ability of the OPLS method to model structured noise. The K-OPLS algorithm allows estimation of an OPLS model in the feature space, thus combining these features. In analogy with the conventional OPLS model, the K-OPLS model contains a set of predictive components Tp and a set of Y-orthogonal components To. This separate modelling of Y-predictive and Y-orthogonal components does not affect the predictive power of the method, which is comparable to KPLS and least-squares SVMs [22]. However, the explicit modelling of structured noise in the feature space can be a valuable tool to detect unexpected anomalies in the data, such as instrumental drift, batch differences or unanticipated biological variation and is not performed by any other kernel-based method to the knowledge of the authors. Pseudo-code for the K-OPLS method is available in Table 1. For further details regarding the K-OPLS method, see Rantalainen et al. [21].
Implementations of various kernel-based methods are available in the literature for the R and MATLAB environments. Among the R packages available on CRAN [23], a few relevant examples include kernlab (kernel-based regression and classification), e1071 (including SVMs) and PLS (implementing a linear kernel-based implementation of the PLS algorithm). kernlab provides a number of kernel-based methods for regression and classification, including SVMs and least-squares SVMs, with functionality for n-fold cross-validation. The e1071 package contains functions for training and prediction using SVMs, including (randomised) n-fold cross-validation. The PLS package includes an implementation of both linear PLS as well as a linear kernel-based PLS version. This enables more efficient computations in situations where the number of observations is very large in relation to the number of features. The PLS package also provides a flexible cross-validation functionality.
The K-OPLS method can be used for both regression as well as classification tasks and has optimal performance in cases where the number of variables is much higher than the number of observations. Typical application areas are non-linear regression and classification problems using omics data sets. Properties of the K-OPLS method make it particularly helpful in cases where detecting and interpreting patterns in the data is of interest. This may e.g. involve instrumental drift over time in metabolic profiling applications using e.g. LC-MS or when there is a risk of dissimilarities between different experimental batches collected at different days. In addition, structured noise (Y-orthogonal variation) may also be present as a result of the biological system itself and can therefore be applied for the explicit detection and modelling of such variation. This is accomplished by interpretation of the Y-predictive and the Y-orthogonal score components in the K-OPLS model. The separation of Y-predictive and Y-orthogonal variation in the feature space is unique to the K-OPLS method and is not present in any other kernel-based method.
The K-OPLS algorithm has been implemented as an open-source and platform-independent software package for MATLAB and R, in accordance with [21]. The K-OPLS package provides functionality for model training, prediction and evaluation using cross-validation. Additionally, model diagnostics and plot functions have been implemented to facilitate and further emphasise the interpretational strengths of the K-OPLS method compared to other related methods.
Kernel methods have previously been applied successfully in many different pattern recognition applications due to the strong predictive abilities and availability of the methods. The K-OPLS method is well suited for analysis of biological data, foremost through its innate capability to separately model predictive variation and structured noise. This property of the K-OPLS method has the potential to improve the interpretation of biological data, as was demonstrated by a plant NMR data set where interpretation is enhanced compared to the related method KPLS. In conjunction with the availability of the outlined open-source package, K-OPLS provides a comprehensive solution for kernel-based analysis in bioinformatics applications.
In this article, we proposed an effective multivariate-based feature filter method for cancer classification, namely, kernelPLS-based filter method. We showed that gene-gene interactions cannot be ignored in feature selection techniques to improve classification performance. In other words the nonlinear relationship of gene-gene interactions is a vital concept that can be taken into account to enhance accuracy. To capture these nonlinear relations of interaction between genes we used kernel method because kernel method can be used to reveal the intrinsic relationships that are hidden in the raw data. In order to capture the reasonable number of components, we make use of the relationship between PLS and linear discriminant analysis to determine the number of components in kernel space based on kernel linear discriminant analysis. To verify the importance of gene-gene interactions we compared our feature selector with other multivariate-based feature selection methods by using two classifiers SVM and KNN. Experimental results, expressed as both accuracy(Acc) and area under the ROC curve(AUC), showed that our method leads to promising improvement in ACC and AUC. We can conclude that the gene-gene interactions whats more, nonlinear relationships of gene-gene interactions are core interactions that can improve classification accuracy, efficiently. We can summarize the characteristics of proposed approach as follows: (1)Fast and efficient. The time complexity of deflation procedure used after the extraction of each component scale is , where is the number of sample. In most cases, the number of sample in microarray data is less than 150, therefore, the running speed of kernelPLS procedure(feature selection time) is faster than others, which are summarized in table 10. (2)Model-free, e.g. no need the distributional assumptions. Because of small sample size, it is difficult to validate distributional assumptions, such as Gaussian distribution, Gamma distribution etc. (3)Applicable to both two-class as well as multi-class classification problems.
So the spatial operator \(\mathcalL\) is approximated by the \(N\times N\) sparse differentiation matrix D having \(N-n\) zero entries and n non-zero entries, where n is the number of centers in the domain \(\Omega_i\). Similarly the boundary operator \(\mathcalB\) can be approximated using the localized kernel-based method as discussed above.
with exact solution \(u(x,t)= e^x t^2\). The problem is solved over the domain \(0 \leq x \leq 1\) at \(t=1\). Different quadrature points are used along the hyperbolic Γ contour. These points are generated by MATLAB statement \(\eta = -M:k:M\) for hyperbolic contour Γ. The parameters used are \(\theta =0.1, \delta =0.1541, r=0.1387, \omega =2, t_0=0.5\) and \(T=5\). The other optimal parameters are given in (4.1). The \(L_\infty \) error and error estimate (E) using fractional orders \(\alpha = 0.8,0.96\) are shown in Table 1. Various numbers of points N in the global domain Ω and n in the local domain \(\Omega_i\) are used. The shape parameter is optimized using the uncertainty principle [36]. The condition number κ, the shape parameter ε and the CPU time(s) are given in the table. It is observed that the proposed method is less sensitive with respect to the shape parameter. The accuracy is achieved for small shape parameter and large condition number. The results are compared with other methods [13]. It is observed that the proposed method is accurate and computationally efficient. This method gives an almost exact solution in time, an error occurs only in spatial discretization. So we can approximate the telegraph equation very accurately in time without any time instability issue. The local nature of the method makes it more attractive for such a type of problems. 2ff7e9595c
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