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has been cited by the following article:

• TITLE: Data Fusion with Optimized Block Kernels in LS-SVM for Protein Classification

  AUTHORS: Li Liao

  KEYWORDS: Data Fusion; Kernel Method; Support Vector Machines; Protein Classification

  JOURNAL NAME: Engineering, Vol.5 No.10B, October 31, 2013

  ABSTRACT: In this work, we developed a method to efficiently optimize the kernel function for combined data of various different sources with their corresponding kernels being already available. The vectorization of the combined data is achieved by a weighted concatenation of the existing data vectors. This induces a kernel matrix composed of the existing kernels as blocks along the main diagonal, weighted according to the corresponding the subspaces span by the data. The induced block kernel matrix is optimized in the platform of least-squares support vector machines simultaneously as the LS-SVM is being trained, by solving an extended set of linear equations, other than a quadratically constrained quadratic programming as in a previous method. The method is tested on a benchmark dataset, and the performance is significantly improved from the highest ROC score 0.84 using individual data source to ROC score 0.92 with data fusion.