no code implementations • 12 Jan 2023 • Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
We first show that the principal component analysis can be improved by entrywise pre-transforming the data matrix if the noise is non-Gaussian, generalizing the known results for the spiked random matrix models with rank-1 signals.
no code implementations • 2 Mar 2022 • Hye Won Chung, Jiho Lee, Ji Oon Lee
For general non-Gaussian noise, assuming that the signal is drawn from the Rademacher prior, we prove that the log likelihood ratio (LR) of the spiked model against the null model converges to a Gaussian when the signal-to-noise ratio is below a certain threshold.
no code implementations • 28 Apr 2021 • Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
We show that the principal component analysis can be improved by pre-transforming the matrix entries if the noise is non-Gaussian.
no code implementations • 16 Jan 2020 • Ji Hyung Jung, Hye Won Chung, Ji Oon Lee
We study the statistical decision process of detecting the signal from a `signal+noise' type matrix model with an additive Wigner noise.
no code implementations • 28 Sep 2018 • Hye Won Chung, Ji Oon Lee
We propose a hypothesis test on the presence of the signal by utilizing the linear spectral statistics of the data matrix.
no code implementations • 4 Sep 2018 • Hye Won Chung, Ji Oon Lee, Do-Yeon Kim, Alfred O. Hero
We define the query difficulty $\bar{d}$ as the average size of the query subsets and the sample complexity $n$ as the minimum number of measurements required to attain a given recovery accuracy.