목차

1. Introduction 1

2 Mutual Adaptation for Heterogeneous Data 5
2.1 Mutual Adaptation 6
2.1.1 Synopsis 8
2.1.2 Formulation and Optimization 12
2.1.3 Pseudo-Labeling 14
2.1.4 Mutual Label Propagation 16
2.2 Experiments 18
2.2.1 Datasets 18
2.2.2 Results for Feature Space Alignment 20
2.2.3 Performance Comparison 22
2.2.4 Ablation Study 27

3 Prospective Adaptation for Longitudinal Data 30
3.1 Prospective Adaptation 31
3.1.1 Feature Transformer 35
3.1.2 Domain Discriminator 36
3.1.3 Label Predictor 36
3.1.4 Optimization 37
3.2 Empirical Study and Evaluation 40
3.2.1 Datasets 40
3.2.2 Experimental Settings 41
3.2.3 Results for Feature Space Alignment 42
3.2.4 Performance Comparison 44
3.3 Application: Alzheimer’s Disease Conversion Prediction 45
3.3.1 Background of Alzheimer’s Disease 45
3.3.2 Formulation: Brain Transition 49
3.3.3 Formulation: Conversion Risk 50
3.3.4 Optimization 51
3.3.5 Experimental Results 54
3.3.6 Enrichment Study 59

4 Multiplex Adaptation for Multi-Modal Data 63
4.1 Multiplex Adaptation 64
4.1.1 Background and Synopsis 67
4.1.2 Formulation 70
4.1.3 Optimization 71
4.1.4 Label Prediction 74
4.2 Experiments 75
4.2.1 Artificial Data – Empirical Study 75
4.2.2 Benchmark Data – Performance Comparison 77
4.3 Application: Historical Faction Identification 79
4.3.1 Background and Data 81
4.3.2 Experimental Settings – Network Construction 83
4.3.3 Results – Performance Comparison 84

5 Conclusion 86

References 89