Unit 5: Dimensionality Reduction, Genetic Algorithms & Reinforcement Learning
Machine Learning Techniques (MCA556) From your syllabus. Dimensionality Reduction In Machine Learning, datasets may contain many features (columns). Example: Student Data ------------ Name Age Gender Address Attendance Marks Projects Activities ... Too many features can: Increase training time Increase memory usage Cause overfitting Dimensionality Reduction reduces the number of features while preserving important information. Benefits Faster computation Less storage Better visualization Reduced overfitting Principal Component Analysis (PCA) Most important dimensionality reduction technique. Purpose: Convert many features into fewer important features. Idea: Preserve maximum variance. Reduce dimensions. Example: 100 Features ↓ PCA ↓ 10 Important Features Applications: Face Recognition Image Compression Data Visualization Linear Discriminant Analysis (LDA) Used for: Dimensionality Reduction Classification Difference: ...