Robust Latent Feature Learning for Incomplete Big Data

Language: English

Published by Springer, Springer Dez 2022, 2022

9811981396 / 9789811981395

Series: Book 21 of 103 - SpringerBriefs in Computer Science

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This item is printed on demand - Print on Demand Titel. Neuware -Incomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty.In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learningusing L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 128 pp. Englisch.

Seller Inventory # 9789811981395

Title
Robust Latent Feature Learning for Incomplete Big Data
Author
Di Wu
Publisher
Springer, Springer Dez 2022
Publication year
2022
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
9811981396
ISBN 13
9789811981395
Item weight
207 grams
Dimensions
235x155x8 mm
Series
Book 21 of 103: SpringerBriefs in Computer Science

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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