Nonlinear dimensionality reduction for visualization

Abstract

The visual interpretation of data is an essential step to guide any further processing or decision making. Dimensionality reduction (or manifold learning) tools may be used for visualization if the resulting dimension is constrained to be 2 or 3. The field of machine learning has developed numerous nonlinear dimensionality reduction tools in the last decades. However, the diversity of methods reflects the diversity of quality criteria used both for optimizing the algorithms, and for assessing their performances. In addition, these criteria are not always compatible with subjective visual quality. Finally, the dimensionality reduction methods themselves do not always possess computational properties that are compatible with interactive data visualization. This paper presents current and future developments to use dimensionality reduction methods for data visualization. © Springer-Verlag 2013.