Search Results for author:
Found 6 papers, 0 papers with code
Detection problems in the spiked matrix models
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.
Asymptotic Normality of Log Likelihood Ratio and Fundamental Limit of the Weak Detection for Spiked Wigner Matrices
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.
Detection of Signal in the Spiked Rectangular Models
We show that the principal component analysis can be improved by pre-transforming the matrix entries if the noise is non-Gaussian.
Weak Detection in the Spiked Wigner Model with General Rank
We study the statistical decision process of detecting the signal from a `signal+noise' type matrix model with an additive Wigner noise.
Weak detection in the spiked Wigner model
We propose a hypothesis test on the presence of the signal by utilizing the linear spectral statistics of the data matrix.
Parity Queries for Binary Classification
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.
