Multiclass relevance vector machines: sparsity and accuracy

Authors:
Ioannis Psorakis;Theodoros Damoulas;Mark A. Girolami
Affiliations:
Department of Engineering Science, University of Oxford, Oxford, UK;Department of Computer Science, Cornell University, Ithaca, NY;Department of Statistical Science, University College of London, London, UK
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

In this paper, we investigate the sparsity and recognition capabilities of two approximate Bayesian classification algorithms, the multiclass multi-kernel relevance vector machines (mRVMs) that have been recently proposed. We provide an insight into the behavior of the mRVM models by performing a wide experimentation on a large range of real-world datasets. Furthermore, we monitor various model fitting characteristics that identify the predictive nature of the proposed methods and compare against existing classification techniques. By introducing novel convergence measures, sample selection strategies and model improvements, it is demonstrated that mRVMs can produce state-of-the-art results on multiclass discrimination problems. In addition, this is achieved by utilizing only a very small fraction of the available observation data.