324 citations · 1k across the 27 of their papers we have counts for
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Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting
Arvind Prasadan, Raj Rao Nadakuditi, Debashis Paul
Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR th…
Time Series Source Separation using Dynamic Mode Decomposition
Arvind Prasadan, Raj Rao Nadakuditi
The Dynamic Mode Decomposition (DMD) extracted dynamic modes are the non-orthogonal eigenvectors of the matrix that best approximates the one-step temporal evolution of the multiva…
OptShrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
Raj Rao Nadakuditi
The truncated singular value decomposition (SVD) of the measurement matrix is the optimal solution to the_representation_ problem of how to best approximate a noisy measurement mat…
When are the most informative components for inference also the principal components?
Raj Rao Nadakuditi
Which components of the singular value decomposition of a signal-plus-noise data matrix are most informative for the inferential task of detecting or estimating an embedded low-ran…
Sample eigenvalue based detection of high dimensional signals in white noise using relatively few samples
N. Raj Rao, Alan Edelman
We present a mathematically justifiable, computationally simple, sample eigenvalue based procedure for estimating the number of high-dimensional signals in white noise using relati…