Robust latent feature learning for incomplete big data / / Di Wu

• Record Nr.: UNINA9910634045203321
• Autore: Wu Di
• Pubbl/distr/stampa: Singapore : , : Springer, , [2023]; ©2023
• ISBN: 9789811981401; 9789811981395
• Descrizione fisica: 1 online resource (119 pages) : illustrations
• Collana: SpringerBriefs in computer science
• Disciplina: 170
• Soggetti: Big data
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- 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 -- 3.2 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 -- 4.3 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 L3F´s 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 L3F´s 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 -- 5.3 A Double-Space and Double-Norm Ensembled Latent Feature Model -- 5.3.1 Predictor Based on Inner Poduct 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 -- 6.3.1 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 -- 7.4 Performance Analysis -- 7.4.1 General Settings -- 7.4.2 Comparisons Between PLF and State-of-the-Art Models -- 7.5 Summary -- References -- Chapter 8: Generalized Deep Latent Feature Learning -- 8.1 Overview -- 8.2 Related Work -- 8.3 A Deep Latent Feature Model -- 8.4 Performance Analysis -- 8.4.1 General Settings -- 8.4.2 Effects of Layer Count in DLF -- 8.4.3 Comparison Between DLF and Related Models -- 8.5 Summary -- References -- Chapter 9: Conclusion and Outlook -- 9.1 Conclusion -- 9.2 Outlook.