Feature space dimensionality reduction for the optimization of visualization methods

Visual data mining methods are of great importance in exploratory data analysis having a high potential for mining large databases. As the data feature space is generally n-dimensional, visual data mining relies on dimensionality reduction techniques. This is the case for image feature spaces which can be visualized by giving each data point a location in a three dimensional space. This paper aims to present a comparative study of several dimensionality reduction methods considering as input image feature spaces, in order to detemine an optimal visualization method to illustrate the separation of the classes. At the beginning, to check the performance of the envisaged method, an artificial dataset consisting of random vectors describing six, 20-dimensional Gaussian distributions with spaced means and low variances was generated. Further, two real images datasets are used to evaluate the contributions of dimensionality reduction algorithms related to data visualization. The analysis focuses on the PCA, LDA and t-SNE dimensionality reduction techniques. Our tests are performed on images for which the computed features include the color histogram and Weber descriptors.