Corpus ID: 202558792

Faster Johnson-Lindenstrauss Transforms via Kronecker Products

@article{Jin2019FasterJT,
  title={Faster Johnson-Lindenstrauss Transforms via Kronecker Products},
  author={Ruhui Jin and Tamara G. Kolda and Rachel Ward},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.04801}
}
  • Ruhui Jin, Tamara G. Kolda, Rachel Ward
  • Published in ArXiv 2019
  • Mathematics, Computer Science
  • The Kronecker product is an important matrix operation with a wide range of applications in supporting fast linear transforms, including signal processing, graph theory, quantum computing and deep learning. In this work, we introduce a generalization of the fast Johnson-Lindenstrauss projection for embedding vectors with Kronecker product structure, the \emph{Kronecker fast Johnson-Lindenstrauss transform} (KFJLT). The KFJLT drastically reduces the embedding cost to an exponential factor of the… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Citations

    Publications citing this paper.

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 38 REFERENCES

    A Mathematical Introduction to Compressive Sensing

    VIEW 17 EXCERPTS
    HIGHLY INFLUENTIAL

    Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

    VIEW 17 EXCERPTS
    HIGHLY INFLUENTIAL

    Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

    VIEW 17 EXCERPTS
    HIGHLY INFLUENTIAL

    Fast and scalable polynomial kernels via explicit feature maps

    VIEW 2 EXCERPTS
    HIGHLY INFLUENTIAL

    Matlab tensor toolbox version 3.1

    • B. W. Bader, T. G. Kolda
    • Available online,
    • 2019
    VIEW 1 EXCERPT

    A Practical Randomized CP Tensor Decomposition

    VIEW 2 EXCERPTS

    Optimality of the Johnson-Lindenstrauss Lemma

    VIEW 1 EXCERPT