Dimensionality Reduction Machine Learning Algorithm

Dimensionality Reduction is used to reduce the number of input variables / features in a dataset.

Dimensionality Reduction Techniques

S.No	Techniques	Details
1	Feature Selection Methods	It uses scores / statistical methods for features selection (keep / delete).
2	Matrix Factorization	It is used reduce a dataset matrix into its constituent parts.
3	Manifold Learning	It is used for the perception / visualization of user (convert low-dimensional data to high-dimensional data for data visualization.)
4	Autoencoder Methods	Here model should reproduce correct input.

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