Online multikernel learning based on a triple-norm regularizer for semantic image classification
type: Item
instance of: scholarly article
title: Online multikernel learning based on a triple-norm regularizer for semantic image classification
zbMATH Open document ID: 1394.68293
DOI: 10.1155/2015/346496
author: S. P. Huang
author: Lianwen Jin
author: Yunyu Li
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: Currently image classifiers based on multikernel learning (MKL) mostly use batch approach, which is slow and difficult to scale up for large datasets. In the meantime, standard MKL model neglects the correlations among examples associated with a specific kernel, which makes it infeasible to adjust the kernel combination coefficients. To address these issues, a new and efficient multikernel multiclass algorithm called TripleReg-MKL is proposed in this work. Taking the principle of strong convex optimization into consideration, we propose a new triple-norm regularizer (TripleReg) to constrain the empirical loss objective function, which exploits the correlations among examples to tune the kernel weights. It highlights the application of multivariate hinge loss and a conservative updating strategy to filter noisy samples, thereby reducing the model complexity. This novel MKL formulation is then solved in an online mode using a primal-dual framework. A theoretical analysis of the complexity and convergence of TripleReg-MKL is presented. It shows that the new algorithm has a complexity of \(O \left(CMT\right)\) and achieves a fast convergence rate of \(O \left(\log T / T\right)\). Extensive experiments on four benchmark datasets demonstrate the effectiveness and robustness of this new approach.
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 62H30
Mathematics Subject Classification ID: 65D18
zbMATH DE Number: 6926101
Wikidata QID: Q59118086
describes a project that uses: MNIST
describes a project that uses: SHOGUN
describes a project that uses: AdaBoost.MH
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/346496
OpenAlex ID: W2121792406
cites work: Q3093403
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description: scientific article; zbMATH DE number 6926101