Dimensionality reduction of a feature set is a common preprocessing step used for pattern
recognition and classification applications. Principal Component Analysis (PCA) is one of
the popular methods used, and can be shown to be optimal using different optimality criteria.
However, it has the disadvantage that measurements from all the original features are used
in the projection to the lower dimensional space. This paper proposes a novel method for
dimensionality reduction of a feature set by choosing a subset of the original features that …

–High data dimensionality with redundancy. The dimensionalities of Many data (Gene, Micro
Array, image, weather, etc) are overwhelmingly high, make it impossible for direct analysis.
Dimensionality reduction by be used to exploit the correlation of data. It is necessary before
any feasible analysis can be done.–Compression. There are huge amounts of data and they
keep piling up. Dimensionality reduction provides efficient ways of compression. A famous
application is using eigenfaces to do human face compression.