Isomap is a nonlinear dimensionality reduction method. It is one of several widely used low-dimensional embedding methods.[1] Isomap is used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities.
^ abTenenbaum, Joshua B.; Silva, Vin de; Langford, John C. (22 December 2000). "A Global Geometric Framework for Nonlinear Dimensionality Reduction". Science. 290 (5500): 2319–2323. doi:10.1126/science.290.5500.2319.