Dimension Reduction Techniques for Optimization

Dimension-reduction techniques can be used to optimize a class problems. We investigate various dimensional reduction techniques to find the best method for our data processing. It is thoroughly discussed how features are extracted and selected. We discuss the various factors that must be considered when reducing dimensions in this paper. In the later part of this paper, we discuss in detail the basic techniques such as principal component analysis, kernel principal component analysis and auto encoder.