Sparse Equisigned PCA: Algorithms and Performance Bounds in the Noisy Rank-1 Setting

no code implementations • 22 May 2019 • 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 that depends on the dimensionality of the system and the number of samples.

Time Series Source Separation using Dynamic Mode Decomposition

1 code implementation • 4 Mar 2019 • Arvind Prasadan, Raj Rao Nadakuditi

We show that when the latent time series are uncorrelated at a lag of one time-step then, in the large sample limit, the recovered dynamic modes will approximate, up to a column-wise normalization, the columns of the mixing matrix.

blind source separation Change Point Detection +2