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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01 Dec 2013
TL;DR: Zhang et al. as mentioned in this paper investigated the influence of picture styles on object recognition by making a connection between image descriptors and a pixel mapping function g, and accordingly proposed an adaptive approach based on a g-incorporated kernel descriptor and multiple kernel learning, without estimating or specifying the image styles used in training and testing.
Abstract: Digital images nowadays show large appearance variabilities on picture styles, in terms of color tone, contrast, vignetting, and etc. These `picture styles' are directly related to the scene radiance, image pipeline of the camera, and post processing functions (e.g., photography effect filters). Due to the complexity and nonlinearity of these factors, popular gradient-based image descriptors generally are not invariant to different picture styles, which could degrade the performance for object recognition. Given that images shared online or created by individual users are taken with a wide range of devices and may be processed by various post processing functions, to find a robust object recognition system is useful and challenging. In this paper, we investigate the influence of picture styles on object recognition by making a connection between image descriptors and a pixel mapping function g, and accordingly propose an adaptive approach based on a g-incorporated kernel descriptor and multiple kernel learning, without estimating or specifying the image styles used in training and testing. We conduct experiments on the Domain Adaptation data set, the Oxford Flower data set, and several variants of the Flower data set by introducing popular photography effects through post-processing. The results demonstrate that the proposed method consistently yields recognition improvements over standard descriptors in all studied cases.
3 citations
01 Oct 2017
TL;DR: This work integrated the three most related-cancer data, including gene expression, DNA methylation, and miRNA expression, and demonstrated that the integrative model was more accurate than the ones based on single data type.
Abstract: In the cancer research, a number of stratification methods have been successfully applied and have assisted in the treatment process. Currently, various data types related to the cancer patients have been measured and collected. This fact leads to a great need of data integration for obtaining more comprehensive cancer study. Most of the previous work is based on a single data type and employed a tailor-made method for a specific data type. In this paper, we have proposed an efficient approach, using multiple kernel learning methods, to better stratify cancer patients. We integrated the three most related-cancer data, including gene expression, DNA methylation, and miRNA expression. The model has combined multiple kernel learning methods and dimensionality reduction. The achieved results demonstrated that our integrative model was more accurate than the ones based on single data type. Our work holds a great promise to contribute to theoretical cancer research and effectively support the prevention and prognosis.
3 citations
TL;DR: In this paper , the authors proposed a multitask multiple kernel learning (MKL) algorithm with a clustering of tasks and developed a highly time-efficient solution approach for it based on the Benders decomposition and treating the clustering problem as finding a given number of tree structures in a graph.
Abstract: Multitask multiple kernel learning (MKL) algorithms combine the capabilities of incorporating different data sources into the prediction model and using the data from one task to improve the accuracy on others. However, these methods do not necessarily produce interpretable results. Restricting the solutions to the set of interpretable solutions increases the computational burden of the learning problem significantly, leading to computationally prohibitive run times for some important biomedical applications. That is why we propose a multitask MKL formulation with a clustering of tasks and develop a highly time-efficient solution approach for it. Our solution method is based on the Benders decomposition and treating the clustering problem as finding a given number of tree structures in a graph; hence, it is called the forest formulation. We use our method to discriminate early-stage and late-stage cancers using genomic data and gene sets and compare our algorithm against two other algorithms. The two other algorithms are based on different approaches for linearization of the problem while all algorithms make use of the cutting-plane method. Our results indicate that as the number of tasks and/or the number of desired clusters increase, the forest formulation becomes increasingly favorable in terms of computational performance.
3 citations
19 Aug 2017
TL;DR: The approach is to combine the well known formulations of SVMs and SVDDs and the proposed model is a closed system and always reaches the global optimal solutions.
Abstract: Generalization error rates of support vector machines are closely related to the ratio of radius of sphere which includes all the data and the margin between the separating hyperplane and the data. There are already several attempts to formulate the multiple kernel learning of SVMs using the ratio rather than only the margin. Our approach is to combine the well known formulations of SVMs and SVDDs. The proposed model is a closed system and always reaches the global optimal solutions.
3 citations
TL;DR: The possibility of learning the kernel, for the Least-Squares Support Vector Machine (LS-SVM) classifier, at the first level of inference, i.e. parameter optimisation, is investigated, substantially alleviating the problem of over-fitting the model selection criterion.
3 citations