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Data compression is the most important step in many signal processing and pattern recognition applications. The authors come across very high dimensional data in such applications. Before processing of large-dimensional datasets, the author needs to reduce the dimensions to have lesser storage space and reduced computational complexities while retaining the maximum information. Principal Component Analysis (PCA) is one such technique that helps in reduction of high dimensional data. It is an unsupervised, useful statistical technique that has been successfully used in dimensionality reduction in pattern recognition applications. There are number of ways of performing Principal Component Analysis. This paper reviews the performance of three such methods, Eigen Decomposition, Singular Value Decomposition and Hebbian Neural Networks.
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