000 | 04060nam a22005535i 4500 | ||
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001 | 978-981-19-8140-1 | ||
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008 | 221206s2023 si | s |||| 0|eng d | ||
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_a005.7 _223 |
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_aWu, Di. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIII, 112 p. 1 illus. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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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. | ||
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_fUABC ; _cPerpetuidad |
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650 | 0 |
_aArtificial intelligence _xData processing. |
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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 |
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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 |
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