Implementation of a covariance-based principal component analysis algorithm with a CUDA-enabled graphics processing unit

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.