evgenia rubinshtein (4 Ergebnisse)

Taschenbuch. Zustand: Neu. Optimal Linear Representations of Images Under Diverse Criteria | Geometric Tools for Finding Linear Projections | Evgenia Rubinshtein | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639133998 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

paperback. Zustand: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Kartoniert / Broschiert. Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rubinshtein EvgeniaEvgenia Rubinshtein, Ph.D: Studied Statistics at Florida State nUniversity. Associate Professor at Vladivostok State University nof Economics and Service and at Far Eastern State Technical nUniversity, Vladivostok,.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image analysis often requires dimension reduction before statistical analysis, in order to apply sophisticated procedures. Motivated by eventual applications, a variety of criteria have been proposed: reconstruction error, class separation, non-Gaussianity using kurtosis, sparseness, mutual information, recognition of objects, and their combinations. Although some criteria have analytical solutions, the remaining ones require numerical approaches. We present geometric tools for finding linear projections that optimize a given criterion for a given data set. The main idea is to formulate a problem of optimization on a Grassmann or a Stiefel manifold, and to use differential geometry of the underlying space to construct optimization algorithms. Purely deterministic updates lead to local solutions, and addition of random components allows for stochastic gradient searches that eventually lead to global solutions. We demonstrate these results using several image datasets, including natural images and facial images. This book should be useful for professionals, researches and graduate students in Image Analysis field.