• shivani
• Anuradha Bharti
• Nishant Behar

The need to effectively utilize the information in an era of increasing data volumes will easily overwhelm the interpreter. However, recent developments in the field of pattern recognition provide a means to analyze multiple attributes in a single volume. There has been extensive research in pattern recognition techniques. Principal Component Analysis (PCA) is one of the most widely used unsupervised statistical algorithm. PCA has applications in all areas of statistics and machine learning including noise filtering, gene data analysis, stock market predictions, clustering, dimensionality reduction, face recognition, image compression and visualization. In this paper all the research work of recent two decades to modify PCA is reviewed and all modified techniques are supposed to remove the constraints of standard PCA.

PCA, Pattern – recognition

"A review of modified PCA techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.328-332, March-2019, Available :http://www.jetir.org/papers/JETIR1903D54.pdf

"A review of modified PCA techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp328-332, March-2019, Available at : http://www.jetir.org/papers/JETIR1903D54.pdf