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.