Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data may exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.
Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 51839701
Quantità: Più di 20 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 51839701-n
Quantità: Più di 20 disponibili
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Hardcover. Condizione: new. Hardcover. Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data may exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets. This book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization in multimedia information processing. The authors present methods to address these challenges, examine popular regularization techniques, and explore the relationship between regularization and clustering performance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781837241996
Quantità: 1 disponibili
Da: PBShop.store US, Wood Dale, IL, U.S.A.
HRD. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781837241996
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: As New. Unread book in perfect condition. Codice articolo 51839701
Quantità: Più di 20 disponibili
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
HRD. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Codice articolo L1-9781837241996
Quantità: Più di 20 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 51839701-n
Quantità: Più di 20 disponibili
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. Codice articolo C9781837241996
Quantità: Più di 20 disponibili
Da: Revaluation Books, Exeter, Regno Unito
Hardcover. Condizione: Brand New. 300 pages. 9.21x6.14 inches. In Stock. Codice articolo x-1837241996
Quantità: 2 disponibili
Da: CitiRetail, Stevenage, Regno Unito
Hardcover. Condizione: new. Hardcover. Clustering is a fundamental problem in multimedia information processing. This co-authored book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization, which are highly relevant for multimedia information processing. Multimedia data may exhibit various forms of noise represented from multiple perspectives, making traditional clustering approaches less effective. The authors consider complex conditions such as noise sensitivity and discuss methods to address these challenges in the context of multimedia data. They also examine popular regularization techniques, providing theoretical analyses that demonstrate the relationship between regularization and clustering performance.Matrix Factorization for Multimedia Clustering: Models, techniques, optimization and applications will serve as a solid advanced reference for researchers, scientists, engineers and advanced students who wish to implement practical tasks through clustering formulations. Additionally, the authors provide a detailed description of convergence theory to enable readers to conduct the corresponding algorithm analyses. They investigate novel regularization techniques, such as self-paced learning, optimal graph learning, and diversity regularization, to uncover the geometric structure of data. These techniques are beneficial for enhancing clustering performance. Furthermore, they demonstrate the efficiency of these regularization techniques through theoretical analyses, practical experiments and applications in real-world datasets. This book explores clustering principles through advanced data analysis techniques, such as matrix and tensor factorization in multimedia information processing. The authors present methods to address these challenges, examine popular regularization techniques, and explore the relationship between regularization and clustering performance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781837241996
Quantità: 1 disponibili