Shared Gaussian Process Latent Variables Models - Oxford Brookes ...

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List of Tables 3.1 Toy data: Procrustes score . . . . . . . . . . . . . . . . . . . . . 70 5.1 Error on Poser data . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.2 Error on HumanEva . . . . . . . . . . . . . . . . . . . . . . . . . 112 9
Chapter 1 Introduction Information technology development have lead to a significant expansion in stor- age capabilities of digital content. This has meant that in many application areas where observations used to be scarce we now have access to significant amounts of data. This development has lead to a transition in many fields from the purely model driven paradigm to the data driven approach. In model driven modeling the aim is to explain a specific phenomenon using a model designed for the task at hand. This is different from data-driven modeling where one tries to use observations of the phenomenon to learn a model. For most modeling scenarios the data available is represented in a form defined by the device used to capture the data. This means that the degrees of freedom of the available data is the degrees of freedom of the capturing device not the actual phenomenon. To reduce the ef- fect of the capturing device This often leads to that the data is represented using a very large number of dimensions, often significantly larger then the number of dimensions or degrees of freedoms that the underlying phenomenon has. This has lead to the machine learning field of Dimensionality Reduction. In dimensionality 10
Page 1 and 2: Shared Gaussian Process Latent Vari
Page 3 and 4: Acknowledgements 2
Page 5 and 6: 4 CONTENTS 2.7.2 Training . . . . .
Page 7 and 8: 6 CONTENTS 5.10 Summary . . . . . .
Page 9: 8 LIST OF FIGURES 3.11 Toy data3: l
Page 13 and 14: 12 CHAPTER 1. INTRODUCTION current
Page 15 and 16: Chapter 2 Background 2.1 Introducti
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60 CHAPTER 3. SHARED GP-LVM sponden
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62 CHAPTER 3. SHARED GP-LVM makes m
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64 CHAPTER 3. SHARED GP-LVM where t
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66 CHAPTER 3. SHARED GP-LVM 4 3 2 1
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68 CHAPTER 3. SHARED GP-LVM 1 0.8 0
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70 CHAPTER 3. SHARED GP-LVM Example
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72 CHAPTER 3. SHARED GP-LVM 0.3 0.2
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74 CHAPTER 3. SHARED GP-LVM φ Y X
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76 CHAPTER 3. SHARED GP-LVM leading
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78 CHAPTER 3. SHARED GP-LVM servati
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Chapter 4 NCCA 4.1 Introduction In
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82 CHAPTER 4. NCCA as u Y i = x S
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84 CHAPTER 4. NCCA X Y Z X X S Y Z
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86 CHAPTER 4. NCCA By pre-multiplyi
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88 CHAPTER 4. NCCA 1 0.8 0.6 0.4 0.
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90 CHAPTER 4. NCCA 4.5 Extensions W
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Chapter 5 Applications 5.1 Introduc
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94 CHAPTER 5. APPLICATIONS as optic
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96 CHAPTER 5. APPLICATIONS of an im
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98 CHAPTER 5. APPLICATIONS histogra
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100 CHAPTER 5. APPLICATIONS the dat
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102 CHAPTER 5. APPLICATIONS Figure
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104 CHAPTER 5. APPLICATIONS the GP
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106 CHAPTER 5. APPLICATIONS varianc
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108 CHAPTER 5. APPLICATIONS locatio
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112 CHAPTER 5. APPLICATIONS Error (
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116 CHAPTER 5. APPLICATIONS over th
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118 CHAPTER 5. APPLICATIONS age rep
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120 CHAPTER 6. CONCLUSIONS vated an
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122 CHAPTER 6. CONCLUSIONS a shared
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124 BIBLIOGRAPHY [7] S. Belongie, J
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126 BIBLIOGRAPHY [24] K. Grochow, S
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128 BIBLIOGRAPHY [39] D. MacKay. Ba
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130 BIBLIOGRAPHY [56] H. A. Simon.
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132 BIBLIOGRAPHY [74] S. Wachter an
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