Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification [article]

A. G. Chung, M. J. Shafiee, A. Wong
2016 arXiv   pre-print
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
more » ... ncreased 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.
arXiv:1602.01818v1 fatcat:ttent3p4o5f67i24akcued4h5e