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Scale-Invariant Geometric Data Analysis (Sigda), Marina Girgis, Max Robinson
Scale-Invariant Geometric Data Analysis (Sigda), Marina Girgis, Max Robinson
STAR Program Research Presentations
The purpose of this research is to introduce a new data analysis method called Scale Invariant Geometric Data Analysis (SIGDA). SIGDA has been shown to be more informative than more common data analysis methods, such as Principal Component Analysis (PCA). SIGDA is used to visualize complex data sets in a way that accurately preserves data patterns and behavior. SIGDA is designed to preserve relative ratios in a numerical matrix, and the number of entries has to be more than the total number of rows and columns. Our research involved providing a simple explanation of SIGDA's mathematical process—simple enough for the …