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.

Unsupervised multiple kernel learning for heterogeneous data integration

Abstract: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus meta-kernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single KPCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of this two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Proposed methods are available in the RR package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

Group feature selection in image classification with multiple kernel learning

Abstract: Classification of large amount of images calls for diverse types of features, but employing all possible feature types will create unnecessary computation burden, and may result in reduced classification accuracy. Selecting feature vectors individually is not a feasible solution in this scenario due to the high amount of feature vectors needed for reasonable performance. Instead, this paper proposes a measure that effectively evaluates the relative significance of a feature group, employing the minimum redundancy maximum relevance (mRMR) feature selection. Multiple kernel learning (MKL) is used for combining different feature types in classification, which implicitly also serves an alternative way for weighing the feature groups' importance. Results show the proposed group feature selection better reflects a feature type's importance, and improve upon MKL performance. This study also finds that the convolutional neural network (CNN) features have the best discriminative power among all features, but it is still possible to improve classification accuracy with other well-designed features.