Data Classification with Modified Density Weighted Distance Measure for Diffusion Maps

The 6th ENGII Conference(Workshop6 2014)——Clinical data analysis is of fundamental importance, as classifications and detailed characterizations of diseases help physicians decide suitable management for patients, individually. In our study, we adopt diffusion maps to embed the data into corresponding lower dimensional representation, which integrate the information of potentially nonlinear progressions of the diseases. To deal with non uniformity of the data, we also consider an alternative distance measure based on the estimated local density. Performance of this modification is assessed using artificially generated data. Another clinical dataset that comprises metabolite concentrations measured with magnetic resonance spectroscopy was also classified. The algorithm shows improved results compared with conventional Euclidean distance measure.

关键词: 数据分类 临床资料 扩散映射 不均匀性 距离度量

主讲人:Prof. Yu-Te Wu 机构:Yang Ming University, Taiwan, China

时长:0:12:20 年代:2014年