Formulizing Co Clusters Selection Methods by Babu Kishore (5 results)
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Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Paperback. Condition: Brand New. 68 pages. 8.66x5.91x0.16 inches. In Stock.
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Taschenbuch. Condition: Neu. Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining | D Kishore Babu | Taschenbuch | 68 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9783659716157 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot…]de | Anbieter: preigu.
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Language: English
Published by LAP LAMBERT Academic Publishing Sep 2018, 2018
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Traditional clustering and feature selection methods consider the data matrix as static. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is… to analyze them separately. Such strategy ignores the time-dependent nature of the underlying data. We propose two formulations for evolutionary co-clustering and feature selection based on the fused Lasso regularization. The evolutionary co-clustering formulation is able to identify smoothly varying hidden block structures embedded into the matrices along the temporal dimension. Our formulation is very flexible and allows for imposing smoothness constraints over only one dimension of the data matrices. The evolutionary feature selection formulation can uncover shared features in clustering from time-evolving data matrices. We show that the optimization problems involved are non-convex, non-smooth and non-separable. To compute the solutions efficiently, we develop a two-step procedure that optimizes the objective function iteratively. 68 pp. Englisch.
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Language: English
Published by LAP LAMBERT Academic Publishing Sep 2018, 2018
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Traditional clustering and feature selection methods consider the data matrix as static. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is to…analyze them separately. Such strategy ignores the time-dependent nature of the underlying data. We propose two formulations for evolutionary co-clustering and feature selection based on the fused Lasso regularization. The evolutionary co-clustering formulation is able to identify smoothly varying hidden block structures embedded into the matrices along the temporal dimension. Our formulation is very flexible and allows for imposing smoothness constraints over only one dimension of the data matrices. The evolutionary feature selection formulation can uncover shared features in clustering from time-evolving data matrices. We show that the optimization problems involved are non-convex, non-smooth and non-separable. To compute the solutions efficiently, we develop a two-step procedure that optimizes the objective function iteratively.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 68 pp. Englisch.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Traditional clustering and feature selection methods consider the data matrix as static. However, the data matrices evolve smoothly over time in many applications. A simple approach to learn from these time-evolving data matrices is to a…nalyze them separately. Such strategy ignores the time-dependent nature of the underlying data. We propose two formulations for evolutionary co-clustering and feature selection based on the fused Lasso regularization. The evolutionary co-clustering formulation is able to identify smoothly varying hidden block structures embedded into the matrices along the temporal dimension. Our formulation is very flexible and allows for imposing smoothness constraints over only one dimension of the data matrices. The evolutionary feature selection formulation can uncover shared features in clustering from time-evolving data matrices. We show that the optimization problems involved are non-convex, non-smooth and non-separable. To compute the solutions efficiently, we develop a two-step procedure that optimizes the objective function iteratively.
