Name
Title | ||
|---|---|---|
Gene clustering by structural prior based local factor analysis model under Bayesian Ying-Yang harmony learning |
Abstract | ||
|---|---|---|
We propose a clustering algorithm based on a structural prior based Local Factor Analysis (spLFA) model under the Bayesian Ying-Yang harmony learning, which automatically determines the hidden dimensionalities during parameter learning, reduces the number of free parameters by projecting the mean vectors onto a low dimensional manifold, imposes the sparseness by a Normal-Jeffreys prior. Experiments on the diagnostic research dataset show that BYY-spLFA outperforms the k-means clustering and single-link hierarchical clustering. The experiments on a lymphoma cancer datset further indicate the BYY-spLFA is able to uncover the number of phenotypes correctly and cluster the phenotypes more accurately. In addition, we modify BYY-spLFA to implement supervised learning and preliminarily demonstrate its effectiveness on a Leukemia data for classification. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/BIBM.2010.5706655 | BIBM |
Keywords | DocType | ISSN |
low dimensional manifold,supervised learning,sparse learning,pattern clustering,diseases,diagnostic research dataset,bayes methods,learning (artificial intelligence),genetics,bayesian ying-yang learning,pattern classification,structural prior,data classification,free parameter number reduction,parameter learning,byy-splfa,splfa model,bayesian ying-yang harmony learning,structural prior based local factor analysis model,lymphoma cancer datset,hidden dimensionalities,leukemia data,gene clustering,medical computing,feature selection,normal-jeffreys prior,bioinformatics,patient diagnosis,hierarchical clustering,gene cluster,factor analysis,bayesian methods,accuracy,manifolds,k means clustering,gene expression,learning artificial intelligence,indexes,cancer | Conference | 2156-1125 |
ISBN | Citations | PageRank |
978-1-4244-8307-5 | 0 | 0.34 |
References | Authors | |
5 | 3 | |