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.
The aims of the research reported in this paper are to investigate the potential of principal directions-based approach in supervised and unsupervised frameworks. The structure of a class is represented in terms of the estimates of its principal directions computed from data, the overall dissimilarity of a particular object with a given class being given by the "disturbance" of the structure, when the object is identified as a member of this class. In case of unsupervised framework, the clusters are computed using the estimates of the principal directions. Our attempt uses arguments based on the principal components to refine the basic idea of k-means aiming to assure soundness and homogeneity to the resulted clusters. Each cluster is represented in terms of its skeleton given by a set of orthogonal and unit eigen vectors (principal directions) of sample covariance matrix, a set of principal directions corresponding to the maximum variability of the "cloud" from metric point of view. A series of conclusions experimentally established are exposed in the final section of the paper.
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