Hangjun (4 Ergebnisse)
Matrix Factorization for Multimedia Clustering
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-Fai; Pan, Baicheng
- Hardcover
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EUR 155,14
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Matrix Factorization for Multimedia Clustering
Che, Hangjun; Wang, Xin; He, Xing; Leung, Man-Fai; Pan, Baicheng
- Hardcover
Anbieter: PBShop.store UK, Fairford, GLOS, Vereinigtes KönigreichPBShop.store UK
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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.
Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications
Che, Hangjun/ Wang, Xin/ He, Xing/ Leung, Man-fai/ Pan, Baicheng
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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EUR 163,77
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Hardcover. Zustand: Brand New. 300 pages. 9.21x6.14 inches. In Stock.
Sprache: Englisch
Verlag: Institution Of Engineering & Technology Feb 2026, 2026
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 168,00
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Buch. Zustand: Neu. Neuware - 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 exhi…bit 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.
