Corpus ID: 59336184

A maximum principle argument for the uniform convergence of graph Laplacian regressors

@article{Trillos2019AMP,
  title={A maximum principle argument for the uniform convergence of graph Laplacian regressors},
  author={Nicol{\'a}s Garc{\'i}a Trillos and Ryan Murray},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.10089}
}
  • Nicolás García Trillos, Ryan Murray
  • Published in ArXiv 2019
  • Mathematics, Computer Science
  • We study asymptotic consistency guarantees for a non-parametric regression problem with Laplacian regularization. In particular, we consider $(x_1, y_1), \dots, (x_n, y_n)$ samples from some distribution on the cross product $\mathcal{M} \times \mathbb{R}$, where $\mathcal{M}$ is a $m$-dimensional manifold embedded in $\mathbb{R}^d$. A geometric graph on the cloud $\{x_1, \dots, x_n \}$ is constructed by connecting points that are within some specified distance $\varepsilon_n$. A suitable semi… CONTINUE READING

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 30 REFERENCES

    Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    The game theoretic p-Laplacian and semi-supervised learning with few labels

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL