Abstract
Although Relevance Vector Machine (RVM) is the most popular algorithms in machine learning and computer vision, outliers in the training data make the estimation unreliable. In the paper, a robust RVM model under non-parametric Bayesian framework is proposed. We decompose the noise term in the RVM model into two components, a Gaussian noise term and a spiky noise term. Therefore the observed data is assumed represented as: y= Dw+s+e where Dw is the relevance vector component, of which D is the kernel function matrix and w is the weight matrix, s is the spiky term and e is the Gaussian noise term. A spike-slab sparse prior is imposed on the weight vector, w which gives a more intuitive constraint on the sparsity than the Student’s t-distribution described in the traditional RVM. For the spiky component, s a spike-slab sparse prior is also introduced to recognize outliers in the training data effectively. Several experiments demonstrate the better performance over the RVM regression.
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