Overview / Usage

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. We implemented parallel version of the Jacobi method to calculate SVD used for PCA.

Methodology / Approach

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. We implemented parallel version of the Jacobi method to calculate SVD used for PCA.

Technologies Used

1. Openmp
2. Multithreading

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Repository

https://github.com/vishalbidawatka/IPSC_Image_Compression_Decompression_PCA_Openmp

Other links

• Final_report (https://github.com/vishalbidawatka/IPSC_Image_Compression_Decompression_PCA_Openmp/blob/master/Final_Report.pdf)