High-dimensional data, meaning data that requires more than two or three dimensions to represent, can be difficult to interpret. One approach to simplification is to assume that the data of interest lies within lower-dimensional space. If the data of interest is of low enough dimension, the data can be visualised in the low-dimensional space.

Several different methods are demonstrated: multidimensional scaling (MDS), locally linear embedding (LLE), and isometric mapping (IsoMap).

Spiros Sirmakessis

University of Peloponnese