소장자료
LDR | 03866nam a22005055i 4500 | ||
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020 | ▼a9789811981401▼9978-981-19-8140-1▲ | ||
024 | 7 | ▼a10.1007/978-981-19-8140-1▼2doi▲ | |
050 | 4 | ▼aQ336▲ | |
082 | 0 | 4 | ▼a005.7▼223▲ |
100 | 1 | ▼aWu, Di.▼eauthor.▼4aut▼4http://id.loc.gov/vocabulary/relators/aut▲ | |
245 | 1 | 0 | ▼aRobust Latent Feature Learning for Incomplete Big Data▼h[electronic resource] /▼cby Di Wu.▲ |
250 | ▼a1st ed. 2023.▲ | ||
264 | 1 | ▼aSingapore :▼bSpringer Nature Singapore :▼bImprint: Springer,▼c2023.▲ | |
300 | ▼aXIII, 112 p. 1 illus.▼bonline resource.▲ | ||
336 | ▼atext▼btxt▼2rdacontent▲ | ||
337 | ▼acomputer▼bc▼2rdamedia▲ | ||
338 | ▼aonline resource▼bcr▼2rdacarrier▲ | ||
347 | ▼atext file▼bPDF▼2rda▲ | ||
490 | 1 | ▼aSpringerBriefs in Computer Science,▼x2191-5776▲ | |
505 | 0 | ▼aChapter 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. .▲ | |
520 | ▼aIncomplete 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 learning using 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.▲ | ||
650 | 0 | ▼aArtificial intelligence▼xData processing.▲ | |
650 | 0 | ▼aQuantitative research.▲ | |
650 | 0 | ▼aData mining.▲ | |
650 | 1 | 4 | ▼aData Science.▲ |
650 | 2 | 4 | ▼aData Analysis and Big Data.▲ |
650 | 2 | 4 | ▼aData Mining and Knowledge Discovery.▲ |
710 | 2 | ▼aSpringerLink (Online service)▲ | |
773 | 0 | ▼tSpringer Nature eBook▲ | |
776 | 0 | 8 | ▼iPrinted edition:▼z9789811981395▲ |
776 | 0 | 8 | ▼iPrinted edition:▼z9789811981418▲ |
830 | 0 | ▼aSpringerBriefs in Computer Science,▼x2191-5776▲ | |
856 | 4 | 0 | ▼uhttps://doi.org/10.1007/978-981-19-8140-1▲ |
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Robust Latent Feature Learning for Incomplete Big Data[electronic resource]
자료유형
국외eBook
서명/책임사항
Robust Latent Feature Learning for Incomplete Big Data [electronic resource] / by Di Wu.
개인저자
판사항
1st ed. 2023.
형태사항
XIII, 112 p. 1 illus. online resource.
총서사항
내용주기
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. .
요약주기
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 learning using 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.
주제
ISBN
9789811981401
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