Articles liés à Robust Latent Feature Learning for Incomplete Big Data
Synopsis
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.
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l?auteur
Dr. Di Wu received a Ph.D. degree in Computer Science from Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China, in 2019. He was a visiting scholar from April 2018 to April 2019 at the University of Louisiana, Lafayette, USA. Currently, he is a Professor at the College of Computer and Information Science, Southwest University. His current research interests include data mining, artificial intelligence, and big data. He has published over 50 papers, including 12 IEEE TRANSACTIONS papers, three highly cited paper of ESI, and several top-tier conferences such as AAAI, ICDM, WWW, and IJCAI, etc. His Google Scholar citations are more than 1800, and his H-Index is 23. He is an Associate Editor for Frontiers in Neurorobotics (SCI, IF 3.493). He received the Nomination Award for Excellent Doctoral Dissertation of the Chinese Association for Artificial Intelligence (CAAI).
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Acheter neuf
Afficher cet article
EUR 54,11
EUR 2,26 expédition vers Etats-Unis
Destinations, frais et délais
Résultats de recherche pour Robust Latent Feature Learning for Incomplete Big Data
Image fournie par le vendeur
Robust Latent Feature Learning for Incomplete Big Data
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 45419619-n
Quantité disponible : Plus de 20 disponibles
Image d'archives
Robust Latent Feature Learning for Incomplete Big Data (Paperback)
Edité par
Springer Verlag, Singapore, Singapore, 2022
ISBN 10 : 9811981396
ISBN 13 : 9789811981395
Neuf
Paperback
Edition originale
Vendeur : Grand Eagle Retail, Bensenville, IL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Paperback. Etat : new. Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9789811981395
Quantité disponible : 1 disponible(s)
Image fournie par le vendeur
Robust Latent Feature Learning for Incomplete Big Data
Edité par
Springer, 2022
ISBN 10 : 9811981396
ISBN 13 : 9789811981395
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 45419619
Quantité disponible : Plus de 20 disponibles
Image d'archives
Robust Latent Feature Learning for Incomplete Big Data (SpringerBriefs in Computer Science)
Vendeur : California Books, Miami, FL, Etats-Unis
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur I-9789811981395
Quantité disponible : Plus de 20 disponibles
Image d'archives
Robust Latent Feature Learning for Incomplete Big Data (SpringerBriefs in Computer Science)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. In. N° de réf. du vendeur ria9789811981395_new
Quantité disponible : Plus de 20 disponibles
Image d'archives
Robust Latent Feature Learning for Incomplete Big Data
Vendeur : Chiron Media, Wallingford, Royaume-Uni
Évaluation du vendeur 4 sur 5 étoiles
PF. Etat : New. N° de réf. du vendeur 6666-IUK-9789811981395
Quantité disponible : 10 disponible(s)
Image fournie par le vendeur
Robust Latent Feature Learning for Incomplete Big Data
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. N° de réf. du vendeur 45419619-n
Quantité disponible : Plus de 20 disponibles
Image d'archives
Robust Latent Feature Learning for Incomplete Big Data (SpringerBriefs in Computer Science)
Vendeur : Books Puddle, New York, NY, Etats-Unis
Évaluation du vendeur 4 sur 5 étoiles
Etat : New. 1st ed. 2023 edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26396345356
Quantité disponible : 4 disponible(s)
Image fournie par le vendeur
Robust Latent Feature Learning for Incomplete Big Data
Edité par
Springer, 2022
ISBN 10 : 9811981396
ISBN 13 : 9789811981395
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 45419619
Quantité disponible : Plus de 20 disponibles
Image fournie par le vendeur
Robust Latent Feature Learning for Incomplete Big Data
Edité par
Springer Nature Singapore Dez 2022, 2022
ISBN 10 : 9811981396
ISBN 13 : 9789811981395
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 128 pp. Englisch. N° de réf. du vendeur 9789811981395
Quantité disponible : 2 disponible(s)