One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a lowdimensional manifold embedded in a high-dimensional space. Drawing on the correspondence between the graph Laplacian, the Laplace Beltrami operator on the manifold, and the connections to the heat equation, we propose a geometrically motivated algorithm for representing the highdimensional data. The algorithm provides a computationally efficient approach to nonlinear dimensionality reduction that has locality-preserving properties and a natural connection to clustering. Some potential applications and illustrative examples are discussed.
Laplacian Eigenmaps was developed to consider how to construct a representation for data lying on a low dimensional manifold embedded in a high-dimensional space REF .
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
Neural Computation
Neural Computation
Mathematics Computer Science