Information visualization by dimensionality reduction: a review

Information visualization can be considered a process of transforming similarity relationships between data points to a geometric representation in order to see unseen information. High-dimensionality data sets are one of the main problems of information visualization. Dimensionality Reduction (DR) is therefore a useful strategy to project high-dimensional space onto low-dimensional space, which it can be visualized directly. The application of this technique has several benefits. First, DR can minimize the amount of storage needed by reducing the size of the data sets. Second, it helps to understand the data sets by discarding any irrelevant features, and to focus on the main important features. DR can enable the discovery of rich information, which assists the task of data analysis. Visualization of high-dimensional data sets is widely used in many fields, such as remote sensing imagery, biology, computer vision, and computer graphics. The visualization is a simple way to understand the high-dimensional space because the relationship between original data points is incomprehensible. A large number of DR methods which attempt to minimize the loss of original information. This paper discuss and analys some DR methods to support the idea of dimensionality reduction to get trustworthy visualization.