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Improving Robustness of Latent Feature Learning Using L1-Norm

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Robust Latent Feature Learning for Incomplete Big Data

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

In this era of information explosion, big data are generated from various industrial applications [1–4]. Realizing intelligent recommender systems (RSs) 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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Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, China

    Di Wu

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  1. Di Wu
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Wu, D. (2023). Improving Robustness of Latent Feature Learning Using L1-Norm. In: Robust Latent Feature Learning for Incomplete Big Data. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-19-8140-1_4

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  • DOI: https://doi.org/10.1007/978-981-19-8140-1_4

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  • Online ISBN: 978-981-19-8140-1

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