Nicholas Asendorf

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We consider a matched subspace detection problem where a signal vector residing in an unknown low-rank k subspace is to be detected using a subspace estimate obtained from noisy signal-bearing training data with missing entries. The resulting subspace estimate is inaccurate due to limited training data, missing entries, and additive noise. Recent results(More)
We analyze the performance of a matched subspace detector (MSD) where the test signal vector is assumed to reside in an unknown, low-rank <i>k</i> subspace that must be estimated from finite, noisy, signal-bearing training data. Under both a stochastic and deterministic model for the test vector, subspace estimation errors due to limited training data(More)
We consider two matrix-valued data sets that are modeled as low-rank-correlated-signal-plus-Gaussian noise. When empirical canonical correlation analysis (CCA) is used to infer these latent correlations, there is a broad regime, where this inference will fail, which was classified by Bao and collaborators in the limit of high dimensionality and sample size.(More)
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