Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification
This work addresses the efficiency challenge in kernel-based learning for object classification, but it appears incremental as it builds on existing random projection methods.
The authors tackled the problem of approximating nonlinear kernels with linear feature maps to reduce training and testing time in kernel-based learning, and their Layered Random Projection (LaRP) framework showed notable improvement in object classification performance on MNIST and COIL-100 databases compared to other state-of-the-art methods.
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.