Kernel based Sparse Representation Classifier (KSRC) can classify images with acceptable performance. In addition, Multiple Kernel Learning based SRC (MKL-SRC) computes the weighted sum of multiple kernels in order to construct a unified kernel while the weight of each kernel is calculated as a ...
In recent years kernel sparse representation based classifier (KSRC) has been widely explored in various pattern classification and recognition tasks [30], [32], [34]. These kernel algorithms were actually proposed to improve the performance of sparse representation based classifier (SRC) [27], [...
4. Kernel Sparse Representation Classifier 4.1. Sparse Representation Classification Let a matrix Ai represent features of the ith class for auxiliary training samples, namely, , where m is the feature dimension, and ni is the number of auxiliary training samples of the ith class. The auxiliary ...
suggests that the best features are selected in the non-linear kernel space, compared to the results of other methods, specifically SFS + SVM with an accuracy of 90.8%, in which a sparse feature selection is conducted in the original feature space followed by a non-linear classifier....
and used sparse representation methods for feature fusion, achieving a classification accuracy of 90.875% with the Sparse Representation based Discriminant Analysis classifier ( SRDA ) classifier. These studies have already proven that multi-domain features are beneficial for more accurate classification and...
Specifically, we are interested in training a mapping function via a non-linear kernel which 'elevates' datapoints from the Euclidean space together with attribute vectors into a non-linear high-dimensional Hilbert space in which the classifier can more easily separate datapoints that come from ...
Solving the primal as in (27) leads to a sparse representation. Given an unseen data point x∗, the final classifier can be written as yˆ∗ = sign(wT ϕˆ(x∗) + b0). (28) Given a value M, PV selection concerns the process of selecting a set of PVs which represent ...
(EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic collaborative representation-based classifier (R-...
In this paper, we propose a kernel representation-based nearest neighbor classifier (KRNNC), which can be regarded as a nonlinear extension of improved nearest neighbor classifier (INNC). What's more, we provide a detailed analysis of the classification mechanism of KRNNC, which is considered ...
Combined techniques can also be applied to the identification, treatment and processing of images, in generating systems based on: extreme Learning Machine and Sparse Representation based classification method, have attracted significant attention due to their respective performance characteristics in computer...