Relevance Units Latent Variable Model and Nonlinear Dimensionality Reduction

Junbin Gao, Jun Zhang, David Tien

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


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 that RULVM algorithm possesses considerable computational advantages over GPLVM algorithm.
Original languageEnglish
Pages (from-to)123-135
Number of pages13
JournalIEEE Transactions on Neural Networks
Issue number1
Publication statusPublished - Jan 2010


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