Research on High-Dimensional Data Reduction

2016 ◽  
Vol 9 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Cuihua Tian ◽  
Yan Wang ◽  
Xueqin Lin ◽  
Jing Lin ◽  
Jiangshui Hong
Keyword(s):  
Data Reduction ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals A Robust High-dimensional Data Reduction Method

2010 ◽  
Vol 9 (1) ◽  
pp. 55-60 ◽  
Author(s):  
Longcun Jin ◽  
Wanggen Wan ◽  
Yongliang Wu ◽  
Bin Cui ◽  
Xiaoqing Yu ◽  
...  
Keyword(s):  
Data Reduction ◽  
Reduction Method ◽  
Hyperspectral Image ◽  
High Dimensional Data ◽  
Cognitive Model ◽  
Pure Component ◽  
High Dimensional ◽  
Model Parameters ◽  
Reduction Algorithm ◽  
Data Reduction Method

In this paper, we propose a robust high-dimensional data reduction method. The model assumes that the pixel reflec-tance results from linear combinations of pure component spectra contaminated by an additive noise. The abundance parameters appearing in this model satisfy positivity and additive constraints. These constraints are naturally expressed in a Bayesian literature by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. The proposed algorithm consists of Bayesian inductive cognition part and hierarchical reduction algorithm model part. The pro-posed reduction algorithm based on Bayesian inductive cognitive model is used to decide which dimensions are advantageous and to output the recommended dimensions of the hyperspectral image. The algorithm can be interpreted as a robust reduction inference method for a Bayesian inductive cognitive model. Experimental results on high-dimensional data demonstrate useful properties of the proposed reduction algorithm.

Sign in / Sign up

Export Citation Format

Share Document