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Title Page
Contents
Abstract 11
Chapter 1. Introduction 12
Chapter 2. Background 15
2.1. Autoencoder 15
2.2. Mixture model 18
2.2.1. Gaussian mixture model (GMM) 18
2.3. Deep autoencoding Gaussian mixture model 23
Chapter 3. Skew-normal distribution 27
3.1. Univariate skew-normal distribution 27
3.2. Multivariate skew-normal distribution (MSN) 28
3.2.1. Multivariate truncated normal distribution 29
3.2.2. Parameter estimation 31
Chapter 4. Deep auotoencoding skew-normal mixture model (DASKNM-EM) 34
4.1. Introduction 34
4.2. Compression network 35
4.3. Estimation network 36
Chapter 5. Simulation 42
5.1. Simulation environment 42
5.1.1. Accuracy 44
5.1.2. Baseline model 47
5.2. Simulation results 48
5.2.1. Case 1 48
5.2.2. Case 2 52
5.2.3. Case 3 54
5.2.4. Case 4 58
Chapter 6. Real data examples 61
6.1. Credit card 61
6.2. Satellite 65
Chapter 7. Conclusion 69
References 71
논문요약 74
Figure 2.1. Architecture of Autoencoder 16
Figure 5.1. The z-space for each simulation case. 43
Figure 5.2. Confusion matrix 45
Figure 5.3. Box plots for absolute skewness and kurtosis of z-vector for case 1 and case 2 49
Figure 5.4. The box-plot of F₁ when n is 1500 (left) and when n is 3000 (right) for case 1 51
Figure 5.5. The box-plot of F₁ when n is 1500 (left) and when n is 3000 (right) for case 2 54
Figure 5.6. Box plots for skewness and kurtosis of z-vector for case 3 and case 4 55
Figure 5.7. The box-plot of F₁ when n is 1500 (left) and when n is 3000 (right) for case 3 57
Figure 5.8. The box-plot of F₁ when n is 1500 (left) and when n is 3000 (right) for case 4 59
Figure 6.1. z-space (left) and CDF of energy value(right) of credit card data 62
Figure 6.2. The box plots of F₁ score with 50 times for credit card 64
Figure 6.3. z-space (left) and CDF of energy value (right) of satellite data 67
Figure 6.4. The box plots of F₁ score with 50 times for satellite 67
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