Li Zhang,Wei-Da Zhou,Pei-Chann Chang.Kernel Sparse Representation-Based Classifier.IEEE Transactions on Signal Processing. 2012Li Zhang et al., "Kernel sparse representation-based classifier," IEEE TSP, vol. 60, no. 4, pp. 1684-1695, 2012. 3.1...
In this paper, a method of kernel optimization by maximizing a measure of class separability in the empirical feature space with sparse representation-based classifier (SRC) is proposed to solve the problem of automatically choosing kernel functions and their parameters in kernel learning. The ...
The proposed method is compared with six representative emotion classification methods, including linear discriminant classifier, K-nearest-neighbor, radial basis function neural networks, support vector machines, sparse representation-based classification and kernel sparse representation-based classification. ...
In the classification step, kernel sparse representation classifier is used to address the problem of visual-based vehicle classification. The kernel function maps the features from original space into higher space dimension. The modified active-set algorithm for l1 non-negative least square problem is...
Kernel sparse representation-based classifier (KSRC) has been proposed, which has good representation and classification performance on face image data. Th... Z Li,WD Zhou,FZ Li - 《Multimedia Tools & Applications》 被引量: 208发表: 2015年 Face recognition via Weighted Sparse Representation - Sc...
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...
Joint kernel sparse representationGraph-based semi-supervised learningMaximum mean discrepancyMost of the existing domain adaptation learning (DAL) methods relies... JW Tao,W Hu,S Wen - 《Neural Networks》 被引量: 3发表: 2016年 Genetic variability and character associations in bread wheat (Triticum...
To address this problem, we present a novel adaptive multikernel sparse representation (AMSR) method. First, multiple basic kernel functions are used to map all training samples into high-dimensional Hilbert space, which captures the nonlinear feature similarities of different tactile samples, and ...
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....
[15] proposed a deep method DarkCovidNet for the detection of COVID-19, and the method was used as a classifier for the You Only Look Once (YOLO) real-time object detection system based on Darknet-19. They implemented 17 convolutional layers and introduced different filtering on each ...