Performance Analysis of Metric Multi Dimensional Scaling with Respect to Different Goodness-of-Fit Criteria in the Context of Dimensionality Reduction

Abstract

Data Mining has become one of the most prominent areas of research. Most of the Data Mining tasks are affected by the curse of dimensionality. Dimensionality reduction is one of the solutions to cope up with the curse of dimensionality. The present work analyzes the performance of Metric Multidimensional Scaling technique with respect to different goodness-of-fit criteria such as Stress, Squared stress, Sammon and Strain in the context of dimensionality Reduction. Time and space have been considered as parameters for analyzing the performance of Metric Multidimensional Scaling technique. Images of different sizes have been considered and Metric Multi dimensional Scaling has been applied to them for dimensionality reduction. The results obtained show that the time taken for dimensionality reduction by Metric Multidimensional Scaling with strain criterion is higher than time taken for dimensionality reduction by Metric Multi dimensional Scaling with stress, squared stress and sammon criteria. The size of the images obtained after applying Metric Multidimensional Scaling remains same for all goodness-of-fit criteria.