Titlebook: Robust Latent Feature Learning for Incomplete Big Data; Di Wu Book 2023 The Author(s), under exclusive license to Springer Nature Singapor

手套

书目名称Robust Latent Feature Learning for Incomplete Big Data影响因子(影响力)




书目名称Robust Latent Feature Learning for Incomplete Big Data影响因子(影响力)学科排名




书目名称Robust Latent Feature Learning for Incomplete Big Data网络公开度




书目名称Robust Latent Feature Learning for Incomplete Big Data网络公开度学科排名




书目名称Robust Latent Feature Learning for Incomplete Big Data被引频次




书目名称Robust Latent Feature Learning for Incomplete Big Data被引频次学科排名




书目名称Robust Latent Feature Learning for Incomplete Big Data年度引用




书目名称Robust Latent Feature Learning for Incomplete Big Data年度引用学科排名




书目名称Robust Latent Feature Learning for Incomplete Big Data读者反馈




书目名称Robust Latent Feature Learning for Incomplete Big Data读者反馈学科排名

破裂

Robust Latent Feature Learning for Incomplete Big Data978-981-19-8140-1Series ISSN 2191-5768 Series E-ISSN 2191-5776

GLUT

Improve Robustness of Latent Feature Learning Using Double-Space,In a high dimensional and incomplete (HDI) matrix, the original data is sparse. Among numerous missing data estimation approaches [1–12], latent feature learning (LFL) is widely studied and adopted because of its high efficiency and scalability.

责问

Di WuExposes readers to a novel research perspective regarding incomplete big data analysis.Presents several robust latent feature learning methods for incomplete big data analysis.Achieves efficient and e

硬化

rheumatism

accomplishment

Basis of Latent Feature Learning, services are provided online. Such numerous online services lead to the problem of information overload [1, 2]. Then, an intelligent and efficient system is desired to address such problem [3, 4]. Therefore, as one of the most efficient and effective approaches for addressing information load, the recommender system has attracted much attention.

nauseate

Improving Robustness of Latent Feature Learning Using ,-Norm,s) to filter the required information is a very challenging problem [5, 6]. Up to now, various methods have been proposed to implement an RS, among which collaborative filtering (CF) is very popular [7–13].

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无能的人