Boosted projection: An ensemble of transformation models

Abstract

Computer vision problems usually suffer from a very high dimensionality, which can make it hard to learn classifiers. A way to overcome this problem is to reduce the dimensionality of the input. This work presents a novel method for tackling this problem, referred to as Boosted Projection. It relies on the use of several projection models based on Principal Component Analysis or Partial Least Squares to build a more compact and richer data representation. We conducted experiments in two important computer vision tasks: pedestrian detection and image classification. Our experimental results demonstrate that the proposed approach outperforms many baselines and provides better results when compared to the original dimensionality reduction techniques of partial least squares.