We address the training problem of the sparse
Least Squares Support Vector Machines (SVM) using compressed
sensing. The proposed algorithm regards the support
vectors as a dictionary and selects the important ones that
minimize the residual output error iteratively. A measurement
matrix is also introduced to reduce the computational
cost. The main advantage is that the proposed algorithm
performs model training and support vector selection simultaneously.
The performance of the proposed algorithm
is tested with several benchmark classification problems in
terms of number of selected support vectors and size of
the measurement matrix. Simulation results show that the
proposed algorithm performs competitively when compared
to existing methods.