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|Title:||Implementation of a covariance-based principal component analysis algorithm with a CUDA-enabled graphics processing unit|
|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 ): singular value decomposition (SVD ), 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)|
|Appears in Collections:||Staff Publications|
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