It always increases the overall number of features in the dataset

It transforms features into a lower-dimensional space through aggregation

It randomly removes features to simplify the model

It is only applicable to unsupervised learning methods

It identifies the most relevant features, thereby reducing overfitting, improving model interpretability, and lowering computational cost