Comparative analysis of dimensionality reduction algorithms, case study: PCA

On the basis of the evaluation of local properties of the data many nonlinear techniques have been suggested the field of computer vision. The application of the dimensionality reduction covers many fields like medical, geographical, simulation and many more. I have studied MDS, LLE and LTSA. Overall, the users are allowed to access the search-tools in linear system. A review and systematic comparison of all the existing techniques has been presented in this paper. The outputs have been explained through identification of current non-linear techniques, and suggestions pertaining to the way the performance of nonlinear dimensionality reduction techniques can be improved. The Purpose of this idea is based on the to implement it in manifold fields by analyzing the result of face detector and recognizer for multiple people in real time with Principal Component analysis on eigen face. According to the most recent research, some issues are confronted in the security at public places. The efficiency and accuracy of these problems can be improved with the range and intricacy of camera networks are booming and the audited surroundings have become more and more entangled and crowded. How these emerging challenges are faced is discussed in the paper.