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

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書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data影響因子(影響力)




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data影響因子(影響力)學(xué)科排名




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data被引頻次




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data被引頻次學(xué)科排名




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data年度引用




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data年度引用學(xué)科排名




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data讀者反饋




書(shū)目名稱Robust Latent Feature Learning for Incomplete Big Data讀者反饋學(xué)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.

責(zé)問(wèn)

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].

棲息地

無(wú)能的人