Relevance units machine based on Akaike's information criterion

Jun Zhang; Junbin Gao

doi:10.1117/12.832314

30 October 2009 Relevance units machine based on Akaike's information criterion

Jun Zhang, Junbin Gao

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749624 (2009) https://doi.org/10.1117/12.832314
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

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.

Citation Download Citation

Jun Zhang and Junbin Gao "Relevance units machine based on Akaike's information criterion", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749624 (30 October 2009); https://doi.org/10.1117/12.832314

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