Machine Learning / 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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