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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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01 Nov 2012TL;DR: The experiments on several UCI real data benchmarks show that, the constructed kernel with optimized weights results in high classification accuracy, compared with multiple kernel learning under the framework of support vector machines.
Abstract: This paper proposes a multiple kernel construction method for kernel discriminant analysis The constructed kernel is a linear combination of several base kernels with a constraint on their weights By maximizing the margin maximization criterion (MMC), we present an iterative scheme for weight optimization The experiments on several UCI real data benchmarks show that, the constructed kernel with optimized weights results in high classfication accuracy, compared with multiple kernel learning under the framework of support vector machines The experiments also show that the constructed kernel relaxes parameter selection for kernel discriminant analysis to some extent
TL;DR: Wang et al. as discussed by the authors obtained three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA).
Abstract: In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).
20 Mar 2012
TL;DR: The paper use heterogeneous kernel functions which have different characteristics to resolve the problem of SVM weak generalization ability to learn and improve the SVM classification accuracy.
Abstract: Website accumulates a large number of customer reviews for goods and website services. Support vector machine (SVM) is an effective text categorization method, it has strong generalization ability and high classification accuracy which can be used to track and manage customer reviews. But SVM has some weaknesses which slow training convergence speed and difficult to raise the classification accuracy. The paper use heterogeneous kernel functions which have different characteristics to resolve the problem of SVM weak generalization ability to learn and improve the SVM classification accuracy. Through classify customer reviews, online shopping websites resolve issues of critical analysis about mass customers reviews and effectively improve website service standard. Key words : Customer Review; Text Categorization; SVM; Multiple Kernel Learning
01 Jan 2012
TL;DR: The matrix multiplicative weight update (MWUMKL) algorithm is based on a well-known QCQP formulation and a novel fast matrix exponentiation routine for QCQPs which might be of independent interest.
Abstract: We present a fast algorithm for multiple kernel learning (MKL). Our matrix multiplicative weight update (MWUMKL) algorithm is based on a well-known QCQP formulation [5]. In addition, we propose a novel fast matrix exponentiation routine for QCQPs which might be of independent interest. Our method avoids the use of commercial nonlinear solvers and scales efficiently to large data sets. 1