In this paper, we consider the harmonic extension problem, which is widely
used in many applications of machine learning. We formulate the harmonic extension as
solving a Laplace-Beltrami equation with Dirichlet boundary condition.
We use the point integral method (PIM) to solve the Laplace-Beltrami equation.
The basic idea of the PIM method is to approximate the Laplace equation using
an integral equation, which is easy to be discretized from points.
Based on the integral equation, we found that traditional
graph Laplacian method (GLM) may fail to approximate the harmonic functions in the classical sense.
For the Laplace-Beltrami equation with Dirichlet boundary, we can prove the convergence of the point integral method.
The point integral method is also very easy to implement, which only requires a minor modification of the graph Laplacian.
One important application of the harmonic extension in
machine learning is semi-supervised learning. We run a popular semi-supervised
learning algorithm by Zhu et al. over a couple of well-known datasets
and compare the performance of the aforementioned approaches. Our experiments show
the PIM performs the best. We also apply PIM to an image recovery problem and
show it outperforms GLM. Finally, on a model problem of Laplace-Beltrami equation with Dirichlet boundary, we prove the convergence
of the point integral method.
Inspired by the graph Laplacian and the point integral method, we introduce a novel weighted graph Laplacian method to compute a smooth interpolation function on a point cloud in high dimensional space. The numerical results in semi-supervised learning and image
inpainting show that the weighted graph Laplacian is a reliable and efficient interpolation method. In addition, it is easy to implement and faster than graph Laplacian.
In this paper we integrate semi-local patches and the weighted graph Laplacian into the framework of the low dimensional manifold model.
This approach is much faster than the original LDMM algorithm. The number of iterations is typically reduced from 100 to 10 and the equations in each step are much easier to solve. This new approach is tested in image inpainting and denoising and the results are better than the original LDMM and competitive with state-of-the-art methods.
Zhaojun BaiUniversity of California, DavisJames DemmelUniversity of California, BerkeleyJack DongarraUniversity of TennesseeJulien LangouUniversity of Colorado, DenverJenny WangUniversity of California, Davis
Numerical Analysis and Scientific Computingmathscidoc:1609.25009