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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: In this paper , a multi-view kernel learning approach is proposed that aims to learn a consensus kernel, which efficiently captures the heterogeneous information of individual views as well as depicts the underlying inherent cluster structure.
Abstract: Gene expression data sets and protein-protein interaction (PPI) networks are two heterogeneous data sources that have been extensively studied, due to their ability to capture the co-expression patterns among genes and their topological connections. Although they depict different traits of the data, both of them tend to group co-functional genes together. This phenomenon agrees with the basic assumption of multi-view kernel learning, according to which different views of the data contain a similar inherent cluster structure. Based on this inference, a new multi-view kernel learning based disease gene identification algorithm, termed as DiGId, is put forward. A novel multi-view kernel learning approach is proposed that aims to learn a consensus kernel, which efficiently captures the heterogeneous information of individual views as well as depicts the underlying inherent cluster structure. Some low-rank constraints are imposed on the learned multi-view kernel, so that it can effectively be partitioned into $k$ or fewer clusters. The learned joint cluster structure is used to curate a set of potential disease genes. Moreover, a novel approach is put forward to quantify the importance of each view. In order to demonstrate the effectiveness of the proposed approach in capturing the relevant information depicted by individual views, an extensive analysis is performed on four different cancer-related gene expression data sets and PPI network, considering different similarity measures.
Journal ArticleDOI
TL;DR: Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs as discussed by the authors , and most of the existing kernel-based learning methods require kernels to be symmetric, which prevents the use of asymmetric kernels.
Abstract: Asymmetric kernels naturally exist in real life, e.g., for conditional probability and directed graphs. However, most of the existing kernel-based learning methods require kernels to be symmetric, which prevents the use of asymmetric kernels. This paper addresses the asymmetric kernel-based learning in the framework of the least squares support vector machine named AsK-LS , resulting in the first classification method that can utilize asymmetric kernels directly. We will show that AsK-LS can learn with asymmetric features, namely source and target features, while the kernel trick remains applicable, i.e., the source and target features exist but are not necessarily known. Besides, the computational burden of AsK-LS is as cheap as dealing with symmetric kernels. Experimental results on various tasks, including Corel, PASCAL VOC, Satellite, directed graphs, and UCI database, all show that in the case asymmetric information is crucial, the proposed AsK-LS can learn with asymmetric kernels and performs much better than the existing kernel methods that rely on symmetrization to accommodate asymmetric kernels.
Book ChapterDOI
14 Nov 2017
TL;DR: A new composite kernel support vector machine (CKSVM) based method to extract significant common spatial pattern (CSP) feature components from multiple temporal-frequency segments in a data-driven manner and introduces a multiple kernel discriminant analysis (MKDA) method for MI EEG classification.
Abstract: High-performance feature engineering and classification algorithms are significantly important for motor imagery (MI) related brain-computer interface (BCI) applications. In this research, we offer a new composite kernel support vector machine (CKSVM) based method to extract significant common spatial pattern (CSP) feature components from multiple temporal-frequency segments in a data-driven manner. Furthermore, we firstly introduce a multiple kernel discriminant analysis (MKDA) method for MI EEG classification. The experimental results on BCI competition IV data set 2b clearly showed the effectiveness of our method outperforming other similar approaches in the literature.
Book ChapterDOI
22 Nov 2010
TL;DR: This paper forms the multi-class MKL in a bilinear form and proposes a scheme for computationally efficient optimization that makes the method favorably applicable to large-scaled samples in the real-world problems.
Abstract: In this paper, we propose a method of multiple kernel learning (MKL) to inherently deal with multi-class classification problems. The performances of kernel-based classification methods depend on the employed kernel functions, and it is difficult to predefine the optimal kernel. In the framework of MKL, multiple types of kernel functions are linearly integrated with optimizing the weights for the kernels. However, the multi-class problems are rarely incorporated in the formulation and the optimization is time-consuming. We formulate the multi-class MKL in a bilinear form and propose a scheme for computationally efficient optimization. The scheme makes the method favorably applicable to large-scaled samples in the real-world problems. In the experiments on multi-class classification using several datasets, the proposed method exhibits the favorable performance and low computation time compared to the previous methods.
Proceedings Article
01 Dec 2012
TL;DR: A computationally efficient implementation for MCEM, which utilizes sequential quadratic programming, is formulated and is shown to offer comparable verification accuracy with considerable improvement in computational speed.
Abstract: Kernel methods have been successfully used in many practical machine learning problems. However, the problem of choosing a suitable kernel is left to practitioners. One method to select the optimal kernel is to learn a linear combination of element kernels. A framework of multiple kernel learning based on conditional entropy minimization criterion (MCEM) has been proposed and it has been shown to work well for, e.g., speaker recognition tasks. In this paper, a computationally efficient implementation for MCEM, which utilizes sequential quadratic programming, is formulated. Through a comparative experiment to conventional MCEM algorithm on a speaker verification task, the proposed method is shown to offer comparable verification accuracy with considerable improvement in computational speed.

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