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Embedding Random Projections in Regularized Gradient Boosting Machines

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 373)

Abstract

Random Projections are a suitable technique for dimensionality reduction in Machine Learning. In this work, we propose a novel Boosting technique that is based on embedding Random Projections in a regularized gradient boosting ensemble. Random Projections are studied from different points of view: pure Random Projections, normalized and uniform binary. Furthermore, we study the effect to keep or change the dimensionality of the data space. Experimental results performed on synthetic and UCI datasets show that Boosting methods with embedded random data projections are competitive to AdaBoost and Regularized Boosting.

Keywords

Regularization Parameter Gaussian Mixture Model Test Pattern Random Projection Dimensionality Reduction Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Computer Vision Center, BarcelonaSpain
  2. 2.Dept. of Applied Mathematics and Analysis, Computer Vision CenterUniversity of BarcelonaBarcelonaSpain

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