Dimensionality Reduction in Machine Learning
- 2. • Dimensionality reduction is the process of reducing the number of random variables or attributes under consideration. • When the dimension increases, with the sparsity, the distance between two independent points increases. That results in less similarity among the data points which will result in more error when it comes to most of the machine learning and other techniques used in data mining. To compensate we will have to feed very large number of data points but with higher dimensions it’s practically impossible and even it’s possible it will be inefficient.
- 3. Techniques of dimensionality reduction Dimensionality reduction is accomplished based on either feature selection or feature extraction. Feature selection is based on omitting those features from the available measurements which do not contribute to class separability. In other words, redundant and irrelevant features are ignored.
- 4. Feature extraction, on the other hand, considers the whole information content and maps the useful information content into a lower dimensional feature space.
- 5. Why Dimensionality Reduction is Important • Dimensionality reduction brings many advantages to your machine learning data, including: • Fewer features mean less complexity • You will need less storage space because you have fewer data • Fewer features require less computation time • Model accuracy improves due to less misleading data • Algorithms train faster thanks to fewer data • Reducing the data set’s feature dimensions helps visualize the data faster • It removes noise and redundant features
- 6. Dimensionality Reduction Techniques • Here are some techniques machine learning professionals use. • Principal Component Analysis(feature extraction). • PCA extracts a new set of variables from an existing, more extensive set. The new set is called “principal components.” • Backward Feature Elimination. • Forward Feature Selection. • Low Variance Filter. • High Correlation Filter. • Decision Trees.(feature selection) • Random Forest. • Factor Analysis.(feature selection)
- 7. How do you do a PCA? 1.Standardize the range of continuous initial variables 2.Compute the covariance matrix to identify correlations 3.Compute the eigenvectors and eigenvalues of the covariance matrix to identify the principal components 4.Create a feature vector to decide which principal components to keep 5.Recast the data along the principal components axes
- 8. Exercise: • Consider the two dimensional patterns (2, 1), (3, 5), (4, 3), (5, 6), (6, 7), (7, 8). • Compute the principal component using PCA Algorithm.
- 17. Thus, two eigen values are λ1 = 8.22 and λ2 = 0.38. Clearly, the second eigen value is very small compared to the first eigen value. So, the second eigen vector can be left out. Eigen vector corresponding to the greatest eigen value is the principal component for the given data set. So. we find the eigen vector corresponding to eigen value λ1.
- 20. • 𝐴 = 𝜋𝑟2 we project the data points onto the new subspace as- = Projected points are:
- 22. Apply PCA for the following dataset