Techniques of dimensionality reduction
Dimensionality reduction:-
Reducing the number of features keeping in mind the ability to discriminating each data object from the rest of the objects.
Given are "m" number of features (m relatively large) discriminating each "n" objects, the task is to re-describing "n" objects with d<<m (d is relatively smaller than m) number of features which may be some of those "m" features or may be new features computed using "m" features, such that desired (target) or task accomplished (classification, clustering) without loss of data generality.
- Finding out d<<m
- choosing some d out of m features (also called sub-setting or selection).
- computed d from m (also called feature transformation).
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| Fig: Dimensionality reduction 1. |
Feature sub-setting:-
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