Research of Classification Algorithm Based on Local Coordination

摘要：

Most of graph-based methods for semi-supervised learning are transductive, giving predictions for only the unlabeled data in the training set, and not for an arbitrary test point. SLC(Semi-supervised Local Linear Coordinate), which is based on LLC(Local Linear Coordinate) is present here as an inductive method. The mixture of factor analyzers is used to model the raw data set, and the label smoothness over the graph is enforced by local approximation. At last, smooth nonlinear projection is achieved by local affine transformation. Experiment shows the superiority of our proposed method in comparison to others.

关键词： mixture of factor analyzers local linear coordinate semi-supervised classification manifold learning

作者: Liyuan Jia Lei Li Li Huang

作者单位: Department of Computer Science Hunan City University Yiyang,China Department of computer and information technology Henan Xinyang Normal College,Xinyang China Department of science and technology Hunan City University Yiyang,China

会议类型: 国际会议

会议名称: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

会议地点: 南京

会议语种:英文

页码: 642-645

在线出版日期: 2010-08-26（万方平台首次上网日期，不代表论文的发表时间）