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

TL;DR

This paper introduces the LaRP framework, a layered random projection method that models linear kernels and nonlinearities separately, improving object classification efficiency and accuracy on standard datasets.

Contribution

The LaRP framework is a novel layered approach that enhances random projection methods by separating kernel modeling and nonlinearity for better classification performance.

Findings

Improved classification accuracy on MNIST and COIL-100 datasets.

Notable performance gains over existing random projection methods.

Efficient training due to separate modeling of kernels and nonlinearities.

Abstract

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.