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072 7 _aCOM031000
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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
500 _aAcceso multiusuario
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.
541 _fUABC ;
_cPerpetuidad
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 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-981-19-8140-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cLIBRO_ELEC
999 _c260756
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