Dimensionality Reduction
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In statistics, machine learning, and information theory, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration by obtaining a set of principal variables.
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Dimensionality Reduction
- 1. Dimensionality Reduction Pranjut Gogoi Lead Software Consultant Knoldus Software LLP
- 2. Agenda ● Machine Learning and its data ● Dimensionality Reduction ● Use cases of Dimensionality Reduction ● Feature Selection ● Feature Extraction
- 4. About Dimensionality Reduction ● “Curse of dimensionality” ○ High time + space complexity ○ Overfitting ○ Presence of irrelevant data ● What ml algo’s want ○ Uncorrelated data or independent variables ○ Less enough data to predict ● “blessing of dimensionality”
- 5. Feature Selection ● What is Feature Selection? ○ Simplify models to make them easier to interpret by users ○ Shorter training times ○ Avoid the curse of dimensionality ○ Enhanced generalization by reducing overfitting ● Ways to select subsets for feature selections are ○ Optimum method ○ Heuristic method ○ Randomized method ● Feature evaluation methods ○ Filter methods (unsupervised method) ○ Wrapper method (supervised method)
- 6. How Feature Selection works
- 7. Subset Selection ● Forward Selection method ○ It starts with empty data set. ○ Try each remaining feature ○ Estimate classification/regression error for adding each feature ○ Select features that give maximum improvement ○ Stop when there is no significant improvement ● Backward Elimination method ○ Starts with the full feature set ○ Try removing features ○ Drop feature with smallest improvement/impact ○ Stop when there is no significant improvement
- 8. Univariate and MultiVariate ● Univariant ○ Pearson correlation coefficient ○ F-score ○ Chi-square ○ Signal to noise ratio ○ Mutual information ● Multivariat ○ Minimum Redundancy and Maximum Relevance (mRMR) ○ Fast Correlation based Feature Selection (FCBF)
- 9. Feature extraction ● It doesn’t select any subset ● It creates new feature set ● After creation features are uncorrelated among themselves ● Types of feature extractions ○ PCA (Principal Component Analysis) ○ LDA (Linear Discriminant Analysis)
- 11. PCA 1-D
- 12. PCA 2-D
- 13. PCA 3-D
- 14. PC1 2-D
- 15. PCA 1-D
- 16. Eigenvector and eigenvalues ● Principal component = eigenvector ● When we use svd, every eigenvector has a eigenvalue ● Terminate those eigenvector with value 0 ● We will have projection values which could be later on used for reducing the dimensions of other inputs.
- 18. Thank You!!