Manifold Learning Based On Kernel Density Estimation
The problem of unknown high-dimensional density estimation has been considered. It has been suggested that the support of its measure is a low-dimensional data manifold. This problem arises in many data mining tasks. The paper proposes a new geometrically motivated solution to the problem in the framework of manifold learning, including estimation of an unknown support of the density. Firstly, the problem of tangent bundle manifold learning has been solved, which resulted in the transformation of high-dimensional data into their low-dimensional features and estimation of the Riemann tensor on the data manifold. Following that, an unknown density of the constructed features has been estimated with the use of the appropriate kernel approach. Finally, using the estimated Riemann tensor, the final estimator of the initial density has been constructed.