Isomap is a dimensionality reduction technique used in machine learning and data analysis. It helps to visualize high-dimensional data by preserving the geometric structure of the data points. By maintaining the distances between points in a lower-dimensional space, Isomap allows for easier interpretation and analysis of complex datasets. The method works by first constructing a neighborhood graph of the data points, then calculating the shortest paths between them. This process helps to capture the intrinsic geometry of the data. Isomap is particularly useful in applications like image processing and pattern recognition, where understanding the underlying structure is crucial.