A dimensionality reduction approach for the visualization of the cluster space: A trustworthiness evaluation

Abstract

The data mining systems solve the problem of handling Earth Observation archives counting on a feature vectors based description of the data. Increasing the dimensionality of the feature vectors would offer an effective perspective of the dataset's content. The modern systems provide visual exploration of data projecting their high-dimensional feature space in a 3-D space. The dimensionality reduction methods represent the main way to achieve such representation. Several dimensionality reduction methods have been proposed to identify the mapping, bot not all of them retain the same dataset properties. In order to compare their performance, the development of formal measures like “Trustworthiness” or the measures based on Co-ranking matrix was mandatory. These measures objectively evaluate the similarity between the structure detected in the original and the reduced space. In this paper we evaluate six dimensionality reduction methods using “Trustworthiness” and “Continuity” measures. In this regard three datasets have been used: an artificial one and two remote sensing datasets. Each of them have been described by a high-dimensional feature space.