The paper presents some results of the attempt on applying a PCA-based classifier for solving problem of morphological classification of galaxies according to the Hubble scheme.A variant of the PCA based classification method [1] is adapted for solving the problem of galaxy image clustering. The performed tests pointed out that the first 24 principal components contain enough information to assure proper resizing for galaxy classification purposes [2]. The skeletons composed from the first principal components for each class can be computed either by a first order approximation technique or, in case there are few principal components, using an exact method.The final section presents a series of conclusions and experimental results confirming the performance of the proposed algorithm, together with a comparative analysis against the perceptron algorithm.
Abstract. The work proposes a new algorithm for the estimation of the ICA model, an algorithm based on secant method and successive approximations. The first sections briefly present the standard FastICA algorithm based on the Newton method and a new version of the FastICA algorithm. The proposed algorithm to estimate the independent components combines the secant iterations with successive approximations technique. The final section presents the results of a comparative analysis experimentally derived conclusions concerning the performance of the proposed method. The tests were performed of several samples of signal files.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.