Relevance units machine based on Akaike's information criterion
Jun Zhang, Junbin Gao, Jinwen Tian · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
The relevance vector machine (RVM) is a sparse regression kernel model. It not only generates a much sparser model but provides better generalization performance than the standard support vector machine (SVM). Relevance vectors and support vectors are both selected from the input vector set. This may limit model flexibility. Recently, we propose Relevance Units Machine (RUM). RUM treats relevance units (RUs) as part of the parameters of the model. However, the number of RUs must be selected before using RUM. In this paper, we use Akaike's Information Criterion (AIC) to select the number of the RUs. The experiment results show that based on AIC RUM maintains all the advantages of RVM and offers superior sparsity.