A General Framework for High-Dimensional Data Reduction Using Unsupervised Bayesian Model

摘要：

In this papier, we propose a general framework for high-dimensional data reduction using unsupervised Bayesian model. The framework assumes that the pixel reflectance results from linear combinations of pure component spectra contaminated by an additive noise. The constraints are naturally expressed in unsupervised Bayesian literature by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. Experimental results on hyperspectral data demonstrate useful properties of the proposed reduction algorithm.

关键词： Bayesian modal inductive cognitive high-dimensional reduction unsupervised general framework

作者: Longcun Jin Wanggen Wan Yongliang Wu Bin Cui Xiaoqing Yu

作者单位: School of Communication and Information Engineering, Shanghai University,Yanchang Rd 149, Shanghai 200072, China

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 96-101

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）