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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 anddoi:10.1109/bibm.2010.5706655 dblp:conf/bibm/ShiTX10 fatcat:nmx6im6zdfekheboq6hmtdb2wy