In this paper we propose improved, high speed convergence algorithms for principal subspace analysis (PSA) and related principal component analysis (PCA). We have confirmed by computer simulations that applied recursive least squares (RLS) technique together with deflation preprocessing, dramatically improves the performance and reduces the training time to only one epoch for natural images. Furthermore, we have found that the training set can be reduced even to 10% of the total number of pixels, for high resolution images, without substantial loss of accuracy.
|Number of pages||8|
|Journal||Neural Network World|
|Publication status||Published - 1996|