Efficient Dimensionality Reduction Using Random Projection

Dimensionality reduction techniques are especially important in the context of embedded vision systems. A promising dimensionality reduction method for a use in such systems is the random projection. In this paper we explore the performance of therandom projection method, which can be easily used in embedded cameras. Random projection is compared to Principal Component Analysis in the terms of recognition efficiency on the COIL-20 image data set. Results show surprisingly good performance of the random projection in comparison to the principal component analysis even without explicit orthogonalization or normalization of transformation subspace. These results support the use of random projection in our hierarchical feature-distribution scheme in visual-sensor networks, where random projection elegantly solves the problem of shared subspace distribution.