000			02567nam a2200301Ia 4500
000			03305nam a22003255i 4500
001			978-981-19-8140-1
003			DE-He213
005			20240319120752.0
007			cr nn 008mamaa
008			221206s2023 si | s |||| 0|eng d
020			_a9789811981401 _9978-981-19-8140-1
082			_a5.7
100			_aWu, Di. _928186
245			_aRobust Latent Feature Learning for Incomplete Big Data _cby Di Wu. _h[electronic resource] /
250			_a1st ed. 2023.
260			_aSingapore _bSpringer Nature Singapore _c2023
300			_aXIII, 112 p. 1 illus. _bonline resource.
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			_aArtificial intelligence _928187
650			_aData Analysis and Big Data. _928188
650			_aData Mining and Knowledge Discovery. _928189
650			_aData mining. _928190
650			_aData Science. _928191
650			_aQuantitative research. _928192
856			_uhttps://doi.org/10.1007/978-981-19-8140-1
942			_cEBK _2ddc
999			_c14936 _d14936