Please use this identifier to cite or link to this item: https://doi.org/10.1109/IGARSS.2011.6049460
Title: Implementation of a covariance-based principal component analysis algorithm with a CUDA-enabled graphics processing unit
Authors: Zhang, J.
Lim, K.H. 
Keywords: covariance
CUDA
GPU
hyperspectral
PCA
Issue Date: 2011
Citation: Zhang, J., Lim, K.H. (2011). Implementation of a covariance-based principal component analysis algorithm with a CUDA-enabled graphics processing unit. International Geoscience and Remote Sensing Symposium (IGARSS) : 1759-1762. ScholarBank@NUS Repository. https://doi.org/10.1109/IGARSS.2011.6049460
Abstract: There are three major approaches of principle component analysis (PCA [1]): singular value decomposition (SVD [2]), covariance-matrix and iterative method (NIPALS). This paper implemented these methods for medium-sized hyperspectral images [3, 4, and 5] in NVIDIA CUDA and compared the performance between them and their CPU counterparts. It is found that the covariance-matrix approach has a great potential of reaching a real-time performance. © 2011 IEEE.
Source Title: International Geoscience and Remote Sensing Symposium (IGARSS)
URI: http://scholarbank.nus.edu.sg/handle/10635/112870
ISBN: 9781457710056
DOI: 10.1109/IGARSS.2011.6049460
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