Robust Kronecker Product PCA for Spatio-Temporal Covariance Estimation
By: Researcher
Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).
Please contact us in case of a broken link from here
| Authors | Kristjan H. Greenewald, A. Hero |
| Journal/Conference Name | IEEE Transactions on Signal Processing |
| Paper Category | Signal Processing |
| Paper Abstract | Kronecker PCA involves the use of a space versus time Kronecker product decomposition to estimate spatio-temporal covariances. In this paper, the addition of a sparse correction factor is considered, which corresponds to a model of the covariance as a sum of Kronecker products of low (separation) rank and a sparse matrix. This sparse correction extends the diagonally corrected Kronecker PCA of [Greenewald, and Hero, 2014] to allow for sparse unstructured “outliers” anywhere in the covariance matrix, e.g., arising from variables or correlations that do not fit the Kronecker model well, or from sources such as sensor noise or sensor failure. We introduce a robust PCA-based algorithm to estimate the covariance under this model. An extension to Toeplitz temporal factors is also provided, producing a parameter reduction for temporally stationary measurement modeling. High dimensional MSE performance bounds are given for these extensions. Finally, the proposed extension of KronPCA is evaluated on both simulated and real data coming from yeast cell cycle experiments. This establishes the practical utility of robust Kronecker PCA in biological and other applications. |
| Date of publication | 2015 |
| Code Programming Language | MATLAB |
| Comment |
Copyright Researcher 2022