Reducing dimensionality, or dimensionality reduction, entails scaling down a large number of attributes or columns (features) into a smaller number of attributes. The main objective of this technique is to get the best number of features for classification, regression, and other unsupervised approaches. In machine learning, we face a problem called the curse of dimensionality. This is where there is a large number of attributes or features. This means more data, causing complex models and overfitting problems.
Dimensionality reduction helps us to deal with the curse of dimensionality. It can transform data linearly and nonlinearly. Techniques for linear transformations include PCA, linear discriminant analysis, and factor analysis. Non-linear transformations include techniques such as t-SNE, Hessian eigenmaps, spectral embedding, and isometric feature mapping. Dimensionality reduction offers the following benefits:
- It filters redundant and less important...
