Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low di(cid:173) mensional manifold embedded in a higher dimensional space. The algorithm provides a computationally efficient approach to non(cid:173) linear dimensionality reduction that has locality preserving prop(cid:173) erties and a natural connection to clustering. In many areas of artificial intelligence, information retrieval and data mining, one is often confronted with intrinsically low dimensional data lying in a very high di(cid:173) mensional space. For example, gray scale n x n images of a fixed object taken with a moving camera yield data points in rn: n2 . However, the intrinsic dimensionality of the space of all images of t he same object is the number of degrees of freedom of the camera - in fact the space has the natural structure of a manifold embedded in rn: n2 .

Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality preserving properties and a natural connection to clustering.

Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifoldembedded in a higher dimensional space. The algorithm provides a computationally efficient approach to nonlinear dimensionalityreduction that has locality preserving properties and a natural connection to clustering.