1. INTRODUCTION
Feature extraction is a technique that describes a large set of data by
utilizing minimal amount of resources. It builds data to be informative
and non-redundant, facilitating the subsequent learning and
generalization steps, and leading to better human interpretations.
When the input is suspected to be redundant and too large to be
processed then it can be transformed into a minimized set of features
.The selected features contain the relevant data from the input values,
so that the preferred work can be done by using this method instead of
the whole data.
Feature extraction is a method of constructing combinations of the
variables to get the data with sufficient accuracy. It is also called
dimensionality reduction and are used such as ISO map, Multifactor
dimensionality reduction, Independent component analysis, Kernel
Principle Component Analysis, Nonlinear dimensionality reduction, Latent
semantic analysis, Partial least squares, PCA (Principal component
analysis), Multi-linear Principal Component Analysis, Multi-linear
subspace learning, Semi definite embedding and Auto encoder. On
important area where feature extraction can be applied is image
processing. There are also software packages targeting machine learning
applications that focus in feature extraction.