Chemometrics for Pattern Recognition

摘要: Acknowledgements. Preface. 1 Introduction. 1.1 Past, Present and Future. 1.2 About this Book. Bibliography. 2 Case Studies. 2.1 2.2 Datasets, Matrices Vectors. 2.3 Study 1: Forensic Analysis of Banknotes. 2.4 2: Near Infrared Spectroscopic Food. 2.5 3: Thermal Polymers. 2.6 4: Environmental Pollution using Headspace Mass Spectrometry. 2.7 5: Human Sweat Analysed by Gas Chromatography 2.8 6: Liquid Spectrometry Pharmaceutical Tablets. 2.9 7: Atomic Spectroscopy for the Hypertension. 2.10 8: Metabolic Profiling Mouse Urine Extracts. 2.11 9: Nuclear Magnetic Resonance Salival Effect Mouthwash. 2.12 10: Simulations. 2.13 11: Null Dataset. 2.14 12: GCMS Microbiology Scent Marks. 3 Exploratory Data Analysis. 3.1 3.2 Principal Components 3.2.1 Background. 3.2.2 Scores Loadings. 3.2.3 Eigenvalues. 3.2.4 PCA Algorithm. 3.2.5 Graphical Representation. 3.3 Dissimilarity Indices, Co-ordinates Ranking. 3.3.1 Dissimilarity. 3.3.2 3.3.3 3.4 Self Organizing Maps. 3.4.1 3.4.2 SOM 3.4.3 Initialization. 3.4.4 Training. 3.4.5 Map Quality. 3.4.6 Visualization. 4 Preprocessing. 4.1 4.2 Scaling. 4.2.1 Transforming Individual Elements. 4.2.2 Row 4.2.3 Column 4.3 Multivariate Methods Reduction. 4.3.1 Largest Components. 4.3.2 Discriminatory 4.3.3 Partial Least Squares Scores. 4.4 Strategies 4.4.1 Flow Charts. 4.4.2 Level 1. 4.4.3 2. 4.4.4 3. 4.4.5 4. 5 Two Class Classifiers. 5.1 5.1.1 5.1.2 5.1.3 Notation. 5.1.4 Autoprediction Boundaries. 5.2 Euclidean Distance to Centroids. 5.3 Linear Discriminant 5.4 Quadratic 5.5 5.5.1 PLS Method. 5.5.2 5.5.3 PLS-DA. 5.6 Learning Vector Quantization. 5.6.1 Voronoi Tesselation Codebooks. 5.6.2 LVQ1. 5.6.3 LVQ3. 5.6.4 LVQ Illustration Summary Parameters. 5.7 Support Machines. 5.7.1 5.7.2 Kernels. 5.7.3 Controlling Complexity Soft Margin SVMs. 5.7.4 SVM 6 One 6.1 6.2 Based 6.3 PC Models SIMCA. 6.4 Indicators Significance. 6.4.1 Gaussian Density Estimators Chi-Squared. 6.4.2 Hotelling's T . 6.4.3 D-Statistic. 6.4.4 Q-Statistic or Squared Prediction Error. 6.4.5 Visualization D- Q-Statistics Disjoint Models. 6.4.6 Normality What do if it Fails. 6.5 Description. 6.6 Summarizing 6.6.1 Membership Plots. 6.6.2 ROC Curves. 7 Multiclass 7.1 7.2 EDC, LDA QDA. 7.3 LVQ. 7.4 PLS. 7.4.1 PLS2. 7.4.2 PLS1. 7.5 SVM. 7.6 against Decisions. 8 Validation Optimization. 8.1 8.1.1 Validation. 8.1.2 8.2 Classification Abilities, Contingency Tables Related Concepts. 8.2.1 8.2.2 8.2.3 8.3 8.3.1 Testing 8.3.2 Test Training Sets. 8.3.3 Predictions. 8.3.4 Increasing Number Variables Classifier. 8.4 Iterative Approaches 8.4.1 Predictive Ability, Model Stability, Majority Vote Cross Rate. 8.4.2 Iterations. 8.4.3 Set 8.5 Optimizing 8.5.1 Components: Cross-Validation Bootstrap. 8.5.2 Thresholds 8.6 Quantization 8.7 Machine 9 Determining Potential Variables. 9.1 9.1.1 Distributions. 9.1.2 9.1.3 Multilevel Multiway 9.1.4 Sample Sizes. 9.1.5 Modelling after Variable 9.1.6 Preliminary 9.2 Which are most Significant?. 9.2.1 Basic Concepts: Statistical Rank. 9.2.2 T-Statistic Fisher Weights. 9.2.3 Multiple Regression, ANOVA F-Ratio. 9.2.4 Squares. 9.2.5 Relationship between Indicator Functions. 9.3 How Many Significant? 9.3.1 Probabilistic Approaches. 9.3.2 Empirical Methods: Monte Carlo. 9.3.3 Cost/Benefit 10 Bayesian Unequal 10.1 10.2 Bayes' Theorem. 10.3 Extensions 11 Separation Indices. 11.1 11.2 Davies Bouldin Index. 11.3 Silhouette Width Modified Width. 11.3.1 11.3.2 11.4 Overlap Coefficient. 12 Comparing Different Patterns. 12.1 12.2 Correlation Methods. 12.2.1 Mantel Test. 12.2.2 R V 12.3 Consensus PCA. 12.4 Procrustes