Feature selection and dimensionality are different in the sense that the former chooses features from the original data space, while the latter does so from a projected space from the original space. Dimensionality reduction has the following advantages that are similar to feature selection, as follows:
- Reducing the training time of prediction models, as redundant, or correlated features are merged into new ones
- Reducing overfitting for the same reason as previously
- Likely improving performance as prediction models will learn from data with less redundant or correlated features
Again, it is not guaranteed that dimensionality reduction will yield ...
