High Performance Computing of Fast Independent Component Analysis for Hyperspectral Image Dimensionality Reduction on MIC-Based Clusters.
Minquan FangYi YuWeimin ZhangHeng WuMingzhu DengJianbin FangPublished in: ICPP Workshops (2015)
Keyphrases
- independent component analysis
- high performance computing
- dimensionality reduction
- principal component analysis
- hyperspectral images
- subspace projection
- hyperspectral
- input space
- hyperspectral imagery
- massively parallel
- independent components
- data points
- factor analysis
- computing systems
- remote sensing
- feature extraction
- principal components
- hyperspectral data
- blind source separation
- low dimensional
- grid computing
- signal processing
- parallel computing
- lower dimensional
- random projections
- high dimensional data
- clustering algorithm
- high dimensionality
- face recognition
- feature space
- dimension reduction
- linear discriminant analysis
- computing resources
- energy efficiency
- fault tolerance
- multispectral
- kernel pca
- principle component analysis
- high dimensional
- discriminant analysis
- target detection
- computing environments
- multispectral images
- feature selection
- pattern recognition
- cluster analysis
- sparse representation
- energy consumption
- image processing