Tracked shipping to South Africa with premium packaging for just R199 

Ship to
South Africa
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications
Type
Physical Book
Publisher
Author
Language
English
Pages
269
Format
Hardcover
Dimensions
23.4 x 15.6 x 1.6 cm
ISBN13
9781837241996

Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications

Che, Hangjun; Wang, Xin; He, Xing (Author) · Institution of Engineering & Technology · Hardcover

Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications - Che, Hangjun; Wang, Xin; He, Xing

Cheaper New Book Imported to South Africa
Delivery: 27 Aug - 10 Sep Shipping: 14 to 18 business days.
R 2,741
Faster New Book Imported to South Africa
Delivery: 13 Aug - 21 Aug Shipping: 4 to 5 business days.
R 3,080
R 2,741

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Matrix Factorization for Multimedia Clustering: Models, Techniques, Optimization and Applications"

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Hardcover.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews