已有的聚类集算法基本上都是非监督聚类集成算法,这样不能利用已知信息,使得聚类集成的准确性、鲁棒性和稳定性降低.把半监督学习和聚类集成结合起来,设计半监督聚类集成模型来克服这些缺点.主要工作包括:第一,设计了基于贝叶斯网络的半监督聚类集成(semi-supervised cluster ensemble,简称SCE)模型,并对模型用变分法进行了推理求解;第二,在此基础上,给出了EM(expectation maximization)框架下的具体算法;第三,从UCI(University of Ca

There are two main contributions in this paper. The first is to propose a joint random field (JRF) model that extends CRF by introducing auxiliary latent variables to characterize visual scene over time and enhance moving object detection in vide

Provable Learning of Overcomplete Latent Variable Models Semi-supervised and Unsupervised Settings