Covariance Matrix Approximation
Covariance Matrix Approximation is a mathematical technique in spatial statistics that belongs to the class of dimensionality reduction and computational optimization methods, distinguished by its focus on constructing a simplified, computationally efficient representation of a high-dimensional spatial covariance matrix while preserving its essential structural and statistical properties for use in spatial prediction and inference.
637
Publications
53.3K
Citations
1.6K
Authors
707
Institutions
Publications per year
2017–2026
149
Parents
637
<i>K</i>-means clustering via principal component analysis
Chris Ding, Xiaofeng He · 2004 · 1.4K citations
Regularized estimation of large covariance matrices
Peter J. Bickel, Elizaveta Levina · 2006 · 1.3K citations
1–5 of 637
1.6K
8
h-index
King Abdullah University of Science and Technology, Saudi Arabia
6 publications · 336 citations
6
h-index
5
h-index
5
h-index
King Abdullah University of Science and Technology, Saudi Arabia
4 publications · 131 citations
4
h-index
1–5 of 1.6K
707
Stanford University
Stanford, United States
15 h-index
28 publications
23.7K citations

Centre National de la Recherche Scientifique
Paris, France
15 h-index
29 publications
868 citations
Princeton University
Princeton, United States
15 h-index
28 publications
4.3K citations
The University of Tokyo
Tokyo, Japan
12 h-index
20 publications
1.5K citations
University of Michigan
Ann Arbor, United States
10 h-index
20 publications
3.4K citations
Texas A&M University
College Station, United States
10 h-index
20 publications
1.8K citations
Carnegie Mellon University
Pittsburgh, United States
10 h-index
17 publications
790 citations
University of Southern California
Los Angeles, United States
9 h-index
19 publications
1.9K citations
1–8 of 707
22 publications · 2.5K citations
17
h-index
16 publications · 2.8K citations
15
h-index
15 publications · 1.1K citations
14
h-index
13 publications · 404 citations
12
h-index
Repository · 16 publications · 684 citations
12
h-index
1–5