  The approximation of nonlinear kernels via linear feature maps has recently
gained interest due to their applications in reducing the training and testing
time of kernel-based learning algorithms. Current random projection methods
avoid the curse of dimensionality by embedding the nonlinear feature space into
a low dimensional Euclidean space to create nonlinear kernels. We introduce a
Layered Random Projection (LaRP) framework, where we model the linear kernels
and nonlinearity separately for increased training efficiency. The proposed
LaRP framework was assessed using the MNIST hand-written digits database and
the COIL-100 object database, and showed notable improvement in object
classification performance relative to other state-of-the-art random projection
methods.
