Name
Abstract | ||
|---|---|---|
This letter proposes an algorithm for linear whitening that minimizes the mean squared error between the original and whitened data without using the truncated eigendecomposition (ED) of the covariance matrix of the original data. This algorithm uses Lanczos vectors to accurately approximate the major eigenvectors and eigenvalues of the covariance matrix of the original data. The major advantage of the proposed whitening approach is its low computational cost when compared with that of the truncated ED. This gain comes without sacrificing accuracy, as illustrated with an experiment of whitening a high-dimensional fMRI data set. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1162/NECO_a_00578 | Neural Computation |
Field | DocType | Volume |
Data set,Lanczos resampling,Computer science,Mean squared error,Algorithm,Speech recognition,Eigendecomposition of a matrix,Artificial intelligence,Covariance matrix,Eigenvalues and eigenvectors,Machine learning | Journal | 26 |
Issue | ISSN | Citations |
5 | 0899-7667 | 3 |
PageRank | References | Authors |
0.40 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abd-Krim Seghouane | 1 | 193 | 24.99 |
| Yousef Saad | 2 | 1940 | 254.74 |