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001453530 0247_ $$a10.1007/978-981-19-8140-1$$2doi
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001453530 1001_ $$aWu, Di,$$eauthor.
001453530 24510 $$aRobust latent feature learning for incomplete big data /$$cDi Wu.
001453530 264_1 $$aSingapore :$$bSpringer,$$c[2023]
001453530 264_4 $$c©2023
001453530 300__ $$a1 online resource (xiii, 112 pages).
001453530 336__ $$atext$$btxt$$2rdacontent
001453530 337__ $$acomputer$$bc$$2rdamedia
001453530 338__ $$aonline resource$$bcr$$2rdacarrier
001453530 4901_ $$aSpringerBriefs in computer science
001453530 504__ $$aIncludes bibliographical references.
001453530 5050_ $$aIntro -- Preface -- Acknowledgments -- Contents -- About the Author -- Chapter 1: Introduction -- 1.1 Background -- 1.2 Symbols and Notations (Table 1.1) -- 1.3 Book Organization -- References -- Chapter 2: Basis of Latent Feature Learning -- 2.1 Overview -- 2.2 Preliminaries -- 2.3 Latent Feature Learning -- 2.3.1 A Basic LFL Model -- 2.3.2 A Biased LFL Model -- 2.3.3 Algorithms Design -- 2.4 Performance Analysis -- 2.4.1 Evaluation Protocol -- 2.4.2 Discussion -- 2.5 Summary -- References -- Chapter 3: Robust Latent Feature Learning based on Smooth L1-norm -- 3.1 Overview
001453530 5058_ $$a3.2 Related Work -- 3.3 A Smooth L1-Norm Based Latent Feature Model -- 3.3.1 Objective Formulation -- 3.3.2 Model Optimization -- 3.3.3 Incorporating Linear Biases into SL-LF -- 3.4 Performance Analysis -- 3.4.1 General Settings -- 3.4.2 Performance Comparison -- 3.4.2.1 Comparison of Prediction Accuracy -- 3.4.2.2 Comparison of Computational Efficiency -- 3.4.3 Outlier Data Sensitivity Tests -- 3.4.4 The Impact of Hyper-Parameter -- 3.5 Summary -- References -- Chapter 4: Improving Robustness of Latent Feature Learning Using L1-Norm -- 4.1 Overview -- 4.2 Related Work
001453530 5058_ $$a4.3 An L1-and-L2-Norm-Oriented Latent Feature Model -- 4.3.1 Objective Formulation -- 4.3.2 Model Optimization -- 4.3.3 Self-Adaptive Aggregation -- 4.4 Performance Analysis -- 4.4.1 General Settings -- 4.4.2 L3Fś Aggregation Effects -- 4.4.3 Comparison Between L3F and Baselines -- 4.4.3.1 Comparison of Rating Prediction Accuracy -- 4.4.3.2 Comparison of Computational Efficiency -- 4.4.4 L3Fś Robustness to Outlier Data -- 4.5 Summary -- References -- Chapter 5: Improve Robustness of Latent Feature Learning Using Double-Space -- 5.1 Overview -- 5.2 Related Work
001453530 5058_ $$a5.3 A Double-Space and Double-Norm Ensembled Latent Feature Model -- 5.3.1 Predictor Based on Inner Product Space (D2E-LF-1) -- 5.3.2 Predictor on Euclidean Distance Space (D2E-LF-2) -- 5.3.3 Ensemble of D2E-LF-1 and D2E-LF-2 -- 5.3.4 Algorithm Design and Analysis -- 5.4 Performance Analysis -- 5.4.1 General Settings -- 5.4.2 Performance Comparison -- 5.5 Summary -- References -- Chapter 6: Data-characteristic-aware Latent Feature Learning -- 6.1 Overview -- 6.2 Related Work -- 6.2.1 Related LFL-Based Models -- 6.2.2 DPClust Algorithm -- 6.3 A Data-Characteristic-Aware Latent Feature Model
001453530 5058_ $$a6.3.1 Model Structure -- 6.3.2 Step 1: Latent Feature Extraction -- 6.3.3 Step 2: Neighborhood and Outlier Detection -- 6.3.4 Step 3: Prediction -- 6.4 Performance Analysis -- 6.4.1 Prediction Rule Selection -- 6.4.2 Performance Comparison -- 6.5 Summary -- References -- Chapter 7: Posterior-neighborhood-regularized Latent Feature Learning -- 7.1 Overview -- 7.2 Related Work -- 7.3 A Posterior-Neighborhood-Regularized Latent Feature Model -- 7.3.1 Primal Latent Feature Extraction -- 7.3.2 Posterior-Neighborhood Construction -- 7.3.3 Posterior-Neighborhood-Regularized LFL
001453530 506__ $$aAccess limited to authorized users.
001453530 520__ $$aIncomplete big data are frequently encountered in many industrial applications, such as recommender systems, the Internet of Things, intelligent transportation, cloud computing, and so on. It is of great significance to analyze them for mining rich and valuable knowledge and patterns. Latent feature analysis (LFA) is one of the most popular representation learning methods tailored for incomplete big data due to its high accuracy, computational efficiency, and ease of scalability. The crux of analyzing incomplete big data lies in addressing the uncertainty problem caused by their incomplete characteristics. However, existing LFA methods do not fully consider such uncertainty. In this book, the author introduces several robust latent feature learning methods to address such uncertainty for effectively and efficiently analyzing incomplete big data, including robust latent feature learning based on smooth L1-norm, improving robustness of latent feature learning using L1-norm, improving robustness of latent feature learning using double-space, data-characteristic-aware latent feature learning, posterior-neighborhood-regularized latent feature learning, and generalized deep latent feature learning. Readers can obtain an overview of the challenges of analyzing incomplete big data and how to employ latent feature learning to build a robust model to analyze incomplete big data. In addition, this book provides several algorithms and real application cases, which can help students, researchers, and professionals easily build their models to analyze incomplete big data.
001453530 588__ $$aDescription based upon print version of record.
001453530 650_0 $$aBig data.
001453530 655_0 $$aElectronic books.
001453530 77608 $$iPrint version:$$aWu, Di$$tRobust Latent Feature Learning for Incomplete Big Data$$dSingapore : Springer,c2023$$z9789811981395
001453530 830_0 $$aSpringerBriefs in computer science.
001453530 852__ $$bebk
001453530 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-8140-1$$zOnline Access$$91397441.1
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001453530 980__ $$aBIB
001453530 980__ $$aEBOOK
001453530 982__ $$aEbook
001453530 983__ $$aOnline
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