Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining
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Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining (2018)
DE PB NWISBN: 9783659716157 bzw. 3659716154, in Deutsch, LAP Lambert Academic Publishing Sep 2018, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
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.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
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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Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining
DE HC NWISBN: 9783659716157 bzw. 3659716154, in Deutsch, Lap Lambert Academic Publishing, gebundenes Buch, neu.
Lieferung aus: Deutschland, Versandkostenfrei innerhalb von Deutschland.
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 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. Sofort lieferbar Lieferzeit 1-2 Werktage.
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 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. Sofort lieferbar Lieferzeit 1-2 Werktage.
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Symbolbild
Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining (2018)
DE PB NWISBN: 9783659716157 bzw. 3659716154, in Deutsch, 68 Seiten, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Sparbuchladen, [3602074].
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. -, 26.09.2018, Taschenbuch, Neuware, 220x150x4 mm, 117g, 68, Internationaler Versand, offene Rechnung (Vorkasse vorbehalten), Selbstabholung und Barzahlung, PayPal, Banküberweisung.
Von Händler/Antiquariat, Sparbuchladen, [3602074].
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. -, 26.09.2018, Taschenbuch, Neuware, 220x150x4 mm, 117g, 68, Internationaler Versand, offene Rechnung (Vorkasse vorbehalten), Selbstabholung und Barzahlung, PayPal, Banküberweisung.
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Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining (2018)
EN PB NWISBN: 9783659716157 bzw. 3659716154, in Englisch, 68 Seiten, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandfertig in 1 - 2 Werktagen, Versandkostenfrei. Tatsächliche Versandkosten können abweichen.
Von Händler/Antiquariat, expressbuch24.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Von Händler/Antiquariat, expressbuch24.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
5
Symbolbild
Formulizing Co-Clusters &Selection Methods Based On SVD in Data Mining
DE PB NWISBN: 9783659716157 bzw. 3659716154, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: LAP Lambert Academic Publishing | Format: Paperback | Language/Sprache: english | 68 pp.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: LAP Lambert Academic Publishing | Format: Paperback | Language/Sprache: english | 68 pp.
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