| Preface | 5 |
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| Contents | 8 |
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| Part I Classification and Clustering | 25 |
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| Issues of robustness and high dimensionality in cluster analysis | 26 |
| 1 Introduction | 26 |
| 2 Multivariate t Distribution | 29 |
| 3 ML Estimation of Mixtures of t Components | 30 |
| 4 Factor Analysis Model for Dimension Reduction | 31 |
| 5 Mixtures of Normal Factor Analyzers | 32 |
| 6 Mixtures of t Factor Analyzers | 34 |
| 7 Discussion | 36 |
| References | 36 |
| Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories | 39 |
| 1 Introduction | 39 |
| 2 Fuzzy data time arrays, fuzzy multivariate time trajectories and dissimilarity measures | 40 |
| 3 Fuzzy K-means clustering models for fuzzy multivariate time trajectories [ CD03] | 43 |
| 4 Fuzzy K-medoids clustering for fuzzy multivariate time trajectories | 45 |
| 5 Application | 47 |
| References | 50 |
| Bootstrap methods for measuring classification uncertainty in latent class analysis | 52 |
| 1 Introduction | 52 |
| 2 Measures of classification uncertainty | 54 |
| 3 The bootstrap method | 55 |
| 4 Bootstrapping LC models | 56 |
| 5 Applications | 57 |
| 6 Discussion | 60 |
| References | 61 |
| A robust linear grouping algorithm | 63 |
| 1 Introduction | 63 |
| 2 Linear Grouping Algorithm | 64 |
| 3 Robust Linear Grouping Algorithm | 65 |
| 4 Examples | 67 |
| 5 Discussion | 70 |
| References | 72 |
| Computing and using the deviance with classification trees | 74 |
| 1 Introduction | 74 |
| 2 Tree induction principle: an illustrative example | 75 |
| 3 Validating the tree descriptive ability | 77 |
| 4 Computational aspects | 82 |
| 5 Conclusion | 84 |
| References | 84 |
| Estimation procedures for the false discovery rate: a systematic comparison for microarray data | 86 |
| 1 Introduction | 86 |
| 2 The testing problem | 87 |
| 3 The false discovery rate | 88 |
| 4 Estimation procedures | 89 |
| 5 The data sets | 92 |
| 6 Outline of the comparative study | 95 |
| 7 Results and conclusions | 96 |
| Acknowledgment | 98 |
| References | 98 |
| A unifying model for biclustering* | 99 |
| 1 Illustrative Example | 99 |
| 2 Biclustering | 100 |
| 3 A Unifying Biclustering Model | 101 |
| 4 Data Analysis | 103 |
| 5 Concluding Remarks | 104 |
| References | 105 |
| Part II Image Analysis and Signal Processing | 107 |
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| Non-rigid image registration using mutual information | 108 |
| 1 Introduction | 108 |
| 2 Non-rigid registration | 109 |
| 3 The mutual information criterion | 112 |
| 4 Non-rigid registration using mutual information | 113 |
| 5 Validation | 116 |
| References | 117 |
| Musical audio analysis using sparse representations | 121 |
| 1 Introduction | 121 |
| 2 Finding Sparse Representations | 122 |
| 3 Sparse Representations for Music Transcription | 125 |
| 4 Source Separation | 128 |
| 5 Conclusions | 130 |
| Acknowledgements | 130 |
| References | 131 |
| Robust correspondence recognition for computer vision | 134 |
| 1 Introduction | 134 |
| 2 Stability and Digraph Kernels | 138 |
| 3 Properties of Strict Sub-Kernels | 142 |
| 4 A Simple Algorithm for Interval Orientations | 144 |
| 5 Discussion | 144 |
| References | 145 |
| Blind superresolution | 147 |
| 1 Introduction | 147 |
| 2 Mathematical Model | 150 |
| 3 Blind Superresolution | 152 |
| 4 Experiments | 155 |
| 5 Conclusions | 156 |
| Acknowledgment | 157 |
| References | 157 |
| Analysis of Music Time Series | 160 |
| 1 Introduction | 160 |
| 2 Model building | 161 |
| 3 Applied models | 164 |
| 4 Studies | 166 |
| 5 Conclusion | 171 |
| References | 172 |
| Part III Data Visualization | 173 |
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| Tying up the loose ends in simple, multiple, joint correspondence analysis | 174 |
| 1 Introduction | 174 |
| 2 Basic CA theory | 175 |
| 3 Multiple and joint correspondence analysis | 177 |
| 4 Data sets used as illustrations | 177 |
| 5 Measuring variance and comparing different tables | 178 |
| 6 The myth of the influential outlier | 179 |
| 7 The scaling problem in CA | 180 |
| 8 To rotate or not to rotate | 186 |
| 9 Statistical significance of results | 189 |
| 10 Loose ends in MCA and JCA | 191 |
| Acknowledgments | 194 |
| References | 194 |
| 3 dimensional parallel coordinates plot and its use for variable selection | 197 |
| 1 Introduction | 197 |
| 2 Parallel coordinates plot and interactive operations | 198 |
| 3 3 dimensional parallel coordinates plot | 199 |
| 4 Implementation of 3D
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