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Data Modeling
- 1. Data Modeling habdi.com | Hamed Abdi - Nov 2020andazeacademy.ir
- 2. andazeacademy.ir From Chaos to Pattern and Beyond
- 5. andazeacademy.ir Pattern Recognition Classification Recall Clustering Learning Logistic Regression Decision Tree Neural Networks Naive Bayes K-Nearest Neighbors Support Vector Machines K-means K-medoids Density Based Hierarchical 5
- 8. andazeacademy.ir Optimization Problem Decision Decision Boundary Minimum Error Rate Total Expected Cost/Risk 8
- 10. andazeacademy.ir Features Selection Wrapper SFS/GSFS (Forward Selection) SBS (Backward Elimination) Filter Embedded Extraction Linear PCA LDA MDA ICA Factor Analysis Multidimensional Scaling Non-Linear Kernel PCA ISOMAP LLE Neural Networks (MLP) SOM 10
- 14. Facial Coding • Facial Action Coding System (FACS) • Action Units (AU) • Action Descriptors (AD) • Intensity Scoring • A Trace • B Slight • C Marked or pronounced • D Severe or extreme • E Maximum • EMFACS (Emotional Facial Action Coding System) andazeacademy.ir 14
- 16. Facial Action Coding System (FACS) Motion Action Units Description Happiness 6+12 Cheek Raiser, Lip Corner Puller Sadness 1+4+15 Inner Brow Raiser, Brow Lowerer, Lip Corner Depressor Surprise 1+2+5+26 Inner Brow Raiser, Outer Brow Raiser, Upper Lid Raiser, Jaw Drop Fear 1+2+4+5+7+20+26 Inner Brow Raiser, Outer Brow Raiser, Brow Lowerer, Upper Lid Raiser, Lid Tightener, Lip Stretcher, Jaw Drop Anger 4+5+7+23 Brow Lowerer, Upper Lid Raiser, Lid Tightener, Lip Tightener Disgust 9+15+16 Nose Wrinkler, Lip Corner Depressor, Lower Lip Depressor Contempt 12+14 (on one side of the face) Lip Corner Puller, Dimpler andazeacademy.ir 16
- 17. 17
- 18. Emotion Recognition API Tools andazeacademy.ir Tool Class label Technique Attributes Number of face Accuracy Microsoft cognitive services anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise Cloud-Based Emotion Recognition Algorithm. Face rectangle top, left, Height, and width. Individual face, Multiple faces High Kairos smile, dislike, surprise, and attention - Eyes, eyebrows, nose, mouth Moderate Eyris EmoVu facial detection Deep Learning Architecture Individual face, static image - Sky biometric application facial attributes including gender, smile, closed eyes, open mouth, glasses and dark glasses Deep Neural Networks Closed eyes, open mouth, glasses and dark glasses. - Affectiva engaged, amused, surprised or confused. Machine-Learning Algorithms Eyes, nose, mouth, eyebrows, Individual face, Multiple faces, Real time images 90% Emotient joy, surprise, sadness, anger, disgust, contempt, fear, Machine-Learning Algorithms Eyes, eyebrows, nose, mouth 18
- 19. Structural Equation Modeling CB-SEM and PLS-SEM
- 21. andazeacademy.ir 21 Thestatisticalmethods First Generation Exploratory Cluster Analysis Exploratory Factor Analysis Multidimensional Scaling Confirmatory Analysis of Variance Logistic Regression Multiple Regression Confirmatory factor Analysis Second Generation Exploratory PLS-SEM (Partial Least Square) Confirmatory CB-SEM (Covariance-Based)
- 22. andazeacademy.ir 22 Considerations in using SEM 1. Composite Variables 2. Measurement 3. Measurement Scales 4. Coding 5. Data Distributions 1. Nominal 2. Ordinal 3. Interval 4. Ratio
- 23. andazeacademy.ir 23 What about Likert Scale?
- 26. andazeacademy.ir 26 Assessment Measurement Model Reflective Internal Consistency Convergent Validity Discriminant Validity Formative Convergent Validity Collinearity among Indicators Significant and Relevance of Outer Weight Structural Model Coefficient of Determination (R2) Effect Sizes (f2) Predictive Relevance (Q2) Size and Significant of Path Coefficients