Corpus ID: 202734206

Wavelet invariants for statistically robust multi-reference alignment

@inproceedings{Hirn2019WaveletIF,
  title={Wavelet invariants for statistically robust multi-reference alignment},
  author={Matthew Hirn and Anna Little},
  year={2019}
}
  • Matthew Hirn, Anna Little
  • Published 2019
  • Computer Science, Engineering, Mathematics
  • We propose a nonlinear, wavelet based signal representation that is translation invariant and robust to both additive noise and random dilations. Motivated by the multi-reference alignment problem and generalizations thereof, we analyze the statistical properties of this representation given a large number of independent corruptions of a target signal. We prove the nonlinear wavelet based representation uniquely defines the power spectrum but allows for an unbiasing procedure that cannot be… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 51 REFERENCES

    Bispectrum Inversion With Application to Multireference Alignment

    VIEW 5 EXCERPTS
    HIGHLY INFLUENTIAL

    Statistical estimation in the presence of group actions

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Optimal rates of estimation for multi-reference alignment

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    Geometric Scattering for Graph Data Analysis

    VIEW 1 EXCERPT

    Multireference Alignment Is Easier With an Aperiodic Translation Distribution

    VIEW 1 EXCERPT