Laplacian Eigenmaps is a dimensionality reduction technique that uses the graph Laplacian to preserve local neighborhood information in a lower-dimensional representation. It is particularly effective for nonlinear dimensionality reduction, capturing the intrinsic geometry of data by leveraging spectral properties of the graph constructed from the data points.