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Title: A boosting method with gaussian mixtures as base learners in a low-dimension space
Authors: Martín-González, S. I. 
Lorenzo-García, F. D.
Navarro-Mesa, J. L. 
Ravelo-García, A. G. 
Quintana-Morales, P. J. 
Hernández-Pérez, E. 
UNESCO Clasification: 3307 Tecnología electrónica
Issue Date: 2007
Journal: Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006
Conference: 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, MLSP 2006 
Abstract: In this paper we propose a classification method in the context of Boosting called Transformed Space Boosting (TSB). Our aim is to develop the idea of using a combination of Gaussian Mixture Models and transformation matrices to design 'non-weak' base learners in Boosting strategies. The use of transformation matrices makes it possible to do a linear dimensionality reduction from an original space to a transformed one. This leads to a two-steps method where in the first one a single-component mixture is trained in the original space. In the second step, based on the single Gaussian previously trained, we apply the concept of average divergence measure to estimate the transformation matrix. The final classifier achieves an improvement in performance compared to other methods also based on dimensionality reduction. This is clearly seen from the experiments we present which strength the validity of our method and show promising classification scores. © 2006 IEEE.
ISBN: 1424406560
DOI: 10.1109/MLSP.2006.275548
Appears in Collections:Actas de congresos
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