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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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Journal ArticleDOI
TL;DR: A new 3D shape classification and retrieval method, based on a supervised selection of the most significant features in a space of attributed extended Reeb graphs encoding different shape characteristics, is proposed.
Abstract: We propose in this article a new 3D shape classification and retrieval method, based on a supervised selection of the most significant features in a space of attributed extended Reeb graphs encoding different shape characteristics. The similarity between pairs of graphs is addressed through both their representation as set of bags of shortest paths, and the definition of kernels adapted to these descriptions. A multiple kernel learning algorithm is used on this set of kernels to find an optimal linear combination of kernels for classification and retrieval purposes. Results on classical data sets are comparable with the best results of the literature, and the modularity and flexibility of the kernel learning ensure its applicability to a large set of methods.

31 citations

Journal ArticleDOI
TL;DR: An improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems and shows that the proposed method performs better than other state-of-the-art methods.

31 citations

Journal ArticleDOI
TL;DR: Experimental results indicate that the proposed BMKELMs technique achieves the highest classification accuracy values when dealing with multiple features, such as a combination of polarimetric coherency and multi-scale spatial features.
Abstract: In this paper, we propose new approach: Boosted Multiple-Kernel Extreme Learning Machines (BMKELMs), a multiple kernel version of Kernel Extreme Learning Machine (KELM). We apply it to the classification of fully polarized SAR images using multiple polarimetric and spatial features. Compared with other conventional multiple kernel learning methods, BMKELMs exploit KELM with the boosting paradigm coming from ensemble learning (EL) to train multiple kernels. Additionally, different fusion strategies such as majority voting, weighted majority voting, MetaBoost, and ErrorPrune were used for selecting the classification result with the highest overall accuracy. To show the performance of BMKELMs against other state-of-the-art approaches, two L-band fully polarimetric airborne SAR images (Airborne Synthetic Aperture Radar (AIRSAR) data collected by NASA JPL over the Flevoland area of The Netherlands and Electromagnetics Institute Synthetic Aperture Radar (EMISAR) data collected by DLR over Foulum in Denmark) we...

31 citations

Journal ArticleDOI
TL;DR: Based on the algorithm of SVM, multiple kernel learning (MKL) method was developed and it has been proved to perform better than SVM in other areas, and MKL can enhance the decision making of contractor pre-qualification.

31 citations

Journal ArticleDOI
08 Jan 2015
TL;DR: An evaluation of a number of feature selection techniques for classification in a biomedical image texture dataset (2-DE gel images) finds the best technique found is SVM-RFE, with an AUROC score of ($95.88±0.39), but this method is not significantly better than RFE-TREE, R FE-RF and grouped MKL, whilst MKL uses lower number of features, increasing the interpretability of the results.
Abstract: The interpretation of the results in a classification problem can be enhanced, specially in image texture analysis problems, by feature selection techniques, knowing which features contribute more to the classification performance. This paper presents an evaluation of a number of feature selection techniques for classification in a biomedical image texture dataset (2-DE gel images), with the aim of studying their performance and the stability in the selection of the features. We analyse three different techniques: subgroup-based multiple kernel learning (MKL), which can perform a feature selection by down-weighting or eliminating subsets of features which shares similar characteristic, and two different conventional feature selection techniques such as recursive feature elimination (RFE), with different classifiers (naive Bayes, support vector machines, bagged trees, random forest and linear discriminant analysis), and a genetic algorithm-based approach with an SVM as decision function. The different classifiers were compared using a ten times tenfold cross-validation model, and the best technique found is SVM-RFE, with an AUROC score of ( $$95.88 \pm 0.39\,\%$$ ). However, this method is not significantly better than RFE-TREE, RFE-RF and grouped MKL, whilst MKL uses lower number of features, increasing the interpretability of the results. MKL selects always the same features, related to wavelet-based textures, while RFE methods focuses specially co-occurrence matrix-based features, but with high instability in the number of features selected.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202244
202172
2020101
2019113
2018114