Survey on Different Dimensionality Reduction Techniques using Machine Learning Framework

Abstract

The data are available from the cloud storage. For the particular method or algorithm all the data are get uploaded in the server. When we need from the upload data only the essential part is get fetched from the server. This essential data can be fetched using the data reduction process. This process can use the method of Fisher data analysis. It can analysis the data in the cloud path can check the requirement what they need, based upon the need the intrinsic information is fetched. The dimensionality reduction can put forward the exact localization of the data in the server path. This method is said to be Locality projection method. In this paper they proposes the use of the both LPP and the FDA which is the together to form LFDA. This LFDA can easily fetch the essential data from the server among common data with the help of the Eigen value method. The data classification and the visualization can takes the special step in this method to locate the required data. The simulation has been made using the LFDA method for the study.