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008			221206s2023 si | o |||| 0|eng d
020			_a9789811981401
024	7		_a10.1007/978-981-19-8140-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 .B45 _b2023 EB
100	1		_aWu, Di _c(Computer science teacher) _eautor _4http://id.loc.gov/vocabulary/relators/aut _9689423
245	1	0	_aRobust Latent Feature Learning for Incomplete Big Data _cby Di Wu
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9495511 _aDatos masivos
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-8140-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI