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Robust Latent Feature Learning for Incomplete Big Data

53,49 €*

Sofort verfügbar, Lieferzeit: 1-3 Tage

Produktnummer: 18e5b64a7be7514ebdaef04dc3ce44acde
Autor: Wu, Di
Themengebiete: Incomplete big data Incomplete matrix Latent feature learning Missing data estimation Representation learning Robustness
Veröffentlichungsdatum: 08.12.2022
EAN: 9789811981395
Sprache: Englisch
Seitenzahl: 112
Produktart: Kartoniert / Broschiert
Verlag: Springer Singapore
Produktinformationen "Robust Latent Feature Learning for Incomplete Big Data"
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.
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