Relevance units latent variable model and nonlinear dimensionality reduction

Authors:
Junbin Gao;Jun Zhang;David Tien
Affiliations:
School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, Australia;Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, Hubei, China;School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW, Australia
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

A new dimensionality reduction method, called relevance units latent variable model (RULVM), is proposed in this paper. RULVM has a close link with the framework of Gaussian process latent variable model (GPLVM) and it originates from a recently developed sparse kernel model called relevance units machine (RUM). RUM follows the idea of relevance vector machine (RVM) under the Bayesian framework but releases the constraint that relevance vectors (RVs) have to be selected from the input vectors. RUM treats relevance units (RUs) as part of the parameters to be learned from the data. As a result, a RUM maintains all the advantages of RVM and offers superior sparsity. RULVM inherits the advantages of sparseness offered by the RUM and the experimental result shows thatRULVMalgorithm possesses considerable computational advantages over GPLVM algorithm.