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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...

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Bibliographic Details
Main Author: Wu, Di (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Singapore : Springer Nature Singapore : Imprint: Springer, 2023.
Edition:1st ed. 2023.
Series:SpringerBriefs in Computer Science,
Subjects:
Artificial intelligence > Data processing.
Quantitative research.
Data mining.
Data Science.
Data Analysis and Big Data.
Data Mining and Knowledge Discovery.
Online Access:https://doi.org/10.1007/978-981-19-8140-1
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Table of Contents:
  • Chapter 1. Introduction
  • Chapter 2. Basis of Latent Feature Learning
  • Chapter 3. Robust Latent Feature Learning based on Smooth L1-norm
  • Chapter 4. Improving robustness of Latent Feature Learning Using L1-norm
  • Chapter 5. Improve robustness of latent feature learning using double-space
  • Chapter 6. Data-characteristic-aware latent feature learning
  • Chapter 7. Posterior-neighborhood-regularized Latent Feature Learning
  • Chapter 8. Generalized deep latent feature learning
  • Chapter 9. Conclusion and Outlook. .

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