Regularization of covariance matrices in high dimensions is usually either based on a known ordering of variables or ignores the ordering entirely. This paper proposes a method for discovering meaningful orderings of variables based on their correlations using the Isomap, a non-linear dimension reduction technique designed for manifold embeddings. These orderings are then used to construct a sparse covariance estimator, which is block-diagonal and/or banded. Finding an ordering to which banding can be applied is desirable because banded estimators have been shown to be consistent in high dimensions. We show that in situations where the variables do have such a structure, the Isomap does very well at discovering it, and the resulting regularized estimator performs better for covariance estimation than other regularization methods that ignore variable order, such as thresholding. We also propose a bootstrap approach to constructing the neighborhood graph used by the Isomap, and show it leads to better estimation. We illustrate our method on data on protein consumption, where the variables (food types) have a structure but it cannot be easily described a priori, and on a gene expression data set.
Wagaman and Levina REF consider block-diagonal and banded covariance matrices and propose an Isomap method for discovering meaningful orderings of variables.
Discovering Sparse Covariance Structures with the Isomap
Journal of Computational and Graphical Statistics
Mathematics