Reducing the number of features for a given dataset is known as dimensionality reduction. There are many techniques used to reduce dimensionality such as :
* Feature Selection Methods
* Matrix Factorization
* Manifold Learning
* Autoencoder Methods
* Linear Discriminant Analysis (LDA)
* Principal component analysis (PCA)
One of the main reasons for dimensionality reduction is the curse of dimensionality. When the number of features increases, the model becomes more complex. But if the number of datapoints is less, the model will start learning or overfitting the data. The model will not generalize the data. This is known as the curse of dimensionality.
Other benefits of dimensionality reduction include :
* The time and storage space is reduced.
* It becomes easier to visualize and visually represent the data in 2D or 3D.
* Space complexity is reduced.
