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

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

来源：International Conference on Life System Modeling and Simulation/International Conference on Intelligent Computing for Sustainable Energy and Environment, 2010-09-17 to 2010-09-20.

• Bayesian modal
• inductive cognitive
• high-dimensional reduction
• unsupervised
• general framework

In this paper, 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.

• 出版日期2010
• 单位上海大学