Topic
Multiple kernel learning
About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.
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TL;DR: A novel Deep Kernel Supervised Hashing (DKSH) method to learn the hashing representations of nodes for node classification is proposed and significantly outperforms the state-of-the-art baselines over three real world benchmark datasets.
4 citations
Journal Article•
TL;DR: The experimental results reveal that the time consumptions of training and testing are decreased and the classification efficiency is maintained at the same level as the origin after applying the cooperative clustering to multiple kernel SVM.
Abstract: Support vector machine based on multiple kernel learning is proposed due to the learning problems involve multiple and heterogeneous data sources,however,the increase of kernels will increase the computation of multiple kernel learning inevitably.To solve this problem,a new cluster method is presented,which is called cooperative clustering.Applying the cooperative clustering to multiple kernel SVM,the number of support vectors will be reduced,the time complexity of computation is also reduced.The experimental results reveal that the time consumptions of training and testing are decreased and the classification efficiency is maintained at the same level as the origin after applying our method.
4 citations
01 Aug 2017
TL;DR: The proposed evidential fusion algorithm is able to exploit multiple detectors based on different gradient, texture and orientation descriptors and outperforms a fusion solution based on Multiple Kernel Learning on difficult high-density crowd images acquired at Makkah at the height of the Muslim pilgrimage.
Abstract: This paper addresses the problem of pedestrian detection in high-density crowd images, characterized by strong homogeneity and clutter. We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model the spatial imprecision arising from each of the detectors. A first result of our study is that the fusion results underline clearly the good complementarity among the four descriptors we considered for this specific context. Moreover, the proposed algorithm outperforms a fusion solution based on Multiple Kernel Learning on difficult high-density crowd images acquired at Makkah at the height of the Muslim pilgrimage.
4 citations
TL;DR: The approach combines the following kernels: feature‐based, tree, and graph and combines their output with Ranking support vector machine (SVM) and can achieve state‐of‐the‐art performance with respect to the comparable evaluations.
Abstract: Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight combination and optimal weight combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.
4 citations
TL;DR: An appearance model based on kernel ridge regression for visual tracking that outperforms other state-of-the-art tracking methods is proposed.
Abstract: Most of the tracking methods attempt to build up feature spaces to represent the appearance of a target. However, limited by the complex structure of the distribution of features, the feature spaces constructed in a linear manner cannot characterize the nonlinear structure well. We propose an appearance model based on kernel ridge regression for visual tracking. Dense sampling is fulfilled around the target image patches to collect the training samples. In order to obtain a kernel space in favor of describing the target appearance, multiple kernel learning is introduced into the selection of kernels. Under the framework, instead of a single kernel, a linear combination of kernels is learned from the training samples to create a kernel space. Resorting to the circulant property of a kernel matrix, a fast interpolate iterative algorithm is developed to seek coefficients that are assigned to these kernels so as to give an optimal combination. After the regression function is learned, all candidate image patches gathered are taken as the input of the function, and the candidate with the maximal response is regarded as the object image patch. Extensive experimental results demonstrate that the proposed method outperforms other state-of-the-art tracking methods.
4 citations