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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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Proceedings ArticleDOI
18 Dec 2011
TL;DR: Compared to a set of classical multivariate linear classifiers, each one highlighting specific strategies, the smooth MKL-SVM method appeared to be the most powerful to distinguish both very mild and mild AD patients from healthy subjets.
Abstract: Multiple kernel learning (MKL) provides flexibility by considering multiple data views and by searching for the best data representation through a combination of kernels. Clinical applications of neuroimaging have seen recent upsurge of the use of multivariate machine learning methods to predict clinical status. However, they usually do not model structured information, such as cerebral spatial and functional networking, which could improve the predictive capacity of the model and which could be more meaningful for further neuroscientific interpretation. In this study, we applied a MKL-based approach to predict prodromal stage of Alzheimer disease (i.e. early phase of the illness) with prior structured knowledges about the brain spatial neighborhood structure and the brain functional circuits linked to cognitve decline of AD. Compared to a set of classical multivariate linear classifiers, each one highlighting specific strategies, the smooth MKL-SVM method (i.e. Lp MKL-SVM) appeared to be the most powerful to distinguish both very mild and mild AD patients from healthy subjets.
Proceedings ArticleDOI
01 Dec 2017
TL;DR: This paper proposes a tracking framework by leveraging the multiple features via feature combination by using multiple features combined with the LPBoost technique, which is able to compute better combinations than others.
Abstract: Multiple kernel learning methods have been successfully applied to visual tracking by finding the best combination of the kernels using boosting techniques. However, they are still not effective in tracking objects with large appearance variations and are not able to generate qualified combinations. In this paper, we propose a tracking framework by leveraging the multiple features via feature combination. By using multiple features combined with the LPBoost technique, our method is able to compute better combinations than others. This framework exploits both local and global features of image patches, thereby generating more accurate tracking results. In addition, a template update strategy is introduced to let the proposed framework more robust to partial occlusion. Experiments on challenging video sequences demonstrate that the proposed tracking algorithm outperforms several state-of-theart methods in both qualitative and quantitative evaluations.
Journal ArticleDOI
TL;DR: A classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification that was able to provide better performances when compared to the standard classification algorithm and the optimization method affects both the computational time and classification accuracy of this strategy.
Abstract: . Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.
Proceedings ArticleDOI
Shuai Ding1, Chao Zhang1, Xuemei Sun1, Chengmao Zhang1, Mingsong Guo1, Guohao Wang1 
01 Nov 2019
TL;DR: A multi-feature integration method based on multiple kernel learning (MKL) framework for motion recognition which is quite promising in integrating sEMG multi-features to advance the classification performance further for multi-motion recognition.
Abstract: Surface electromyography (sEMG) signals have been used as the inputs of many human-machine interface applications. Different methods for upper limb motion recognition are developed based on sEMG features. The sEMG feature extraction is the key issue in affecting classification performance in multi-motion pattern recognition. A number of features have been extracted to form feature combinations. However, these feature combinations are simple concatenation of multiple features. The combination weights for different sEMG features are equal. It may not help to design an effective multi-motion recognition system. In this study, to overcome the limitation and combine flexibly multiple features, we propose a multi-feature integration method based on multiple kernel learning (MKL) framework for motion recognition. The proposed method integrates the valuable information which can represent the motion patterns. Multiple kinds of features are fused together with optimal combination weights. In the experiment, we test the proposed method on multiple different hand motions. The corresponding experimental results show that the proposed method provides better classification performance than the other frequently used methods of feature combination. The results confirm that the proposed method is quite promising in integrating sEMG multi-features to advance the classification performance further for multi-motion recognition.
Proceedings ArticleDOI
01 Aug 2015
TL;DR: Through extensive experimentation on major benchmarks, it is shown that this adaptive pooling over multiple trajectory attributes leads to significant improvements in recognition performance.
Abstract: We present a new approach for feature pooling in human action recognition. Instead of partitioning videos at predefined uniform intervals in a spatial-temporal volume as done with spatial pyramid matching, our method adaptively partitions in a pooling attribute space, defined by multiple trajectory-based cues. The pooling attributes include individual spatial and temporal coordinates of a trajectory, as well as its motion saliency, curvature, and scale. To determine partitions of the attribute space in an adaptive manner, we utilize KD-trees that separate trajectories based on their distributions within the attribute space. The generated pooling volumes are jointly utilized for action recognition via SVM weights learned by Multiple Kernel Learning. Through extensive experimentation on major benchmarks, it is shown that this adaptive pooling over multiple trajectory attributes leads to significant improvements in recognition performance.

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