Linear Dimensionality Reduction Method for Graph Data

This thesis investigated the graph dimensionality reduction, which is the pre-processing of data analysis. It created efficient linear approaches that address dimensionality reduction. The findings pointed to the new approaches minimising the expenses of processing, and preserving structural properties of data with a high accuracy in a low space.