Image Reduction Using Assorted Dimensionality Reduction Techniques

Dimensionality reduction is the mapping of data from a high dimensional space to a lower dimension space such that the result obtained by analyzing the reduced dataset is a good approximation to the result obtained by analyzing the original data set. There are several dimensionality reduction approaches which include Random Projections, Principal Component Analysis, the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. In this paper, we propose three new techniques, each of which will be a modified version of the last three techniques mentioned above (the Combined and Direct approaches, and the New Random Approach). We shall implement each of the ten reduction techniques mentioned, after which we shall use these techniques to compress various pictures. Finally, we shall compare the ten reduction techniques implemented in this paper with each other by the extent to which they preserve images.