Generalized singular value decomposition

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

In linear algebra, the generalized singular value decomposition (GSVD) is the name of two different techniques based on the singular value decomposition. The two versions differ because one version decomposes two (or more) matrices (much like higher order PCA) and the other version uses a set of constraints imposed on the left and right singular vectors.

Higher order version[edit]

The generalized singular value decomposition (GSVD) is a matrix decomposition more general than the singular value decomposition. It is used to study the conditioning and regularization of linear systems with respect to quadratic semi-norms.

Let , or . Given matrices and , their GSVD is given by


where , and are unitary matrices, and is non-singular, where . Also, is non-negative diagonal, and is non-negative block-diagonal, with diagonal blocks; is not always diagonal. It holds that and , and that . This implies . The ratios are called the generalized singular values of and . If is square and invertible, then the generalized singular values are the singular values, and and are the matrices of singular vectors, of the matrix . Further, if , then the GSVD reduces to the singular value decomposition, explaining the name.

Weighted version[edit]

The weighted version of the generalized singular value decomposition (GSVD) is a constrained matrix decomposition with constraints imposed on the left and right singular vectors of the singular value decomposition.[1][2][3] This form of the GSVD is an extension of the SVD as such. Given the SVD of an m×n real or complex matrix M


Where I is the Identity Matrix and where and are orthonormal given their constraints ( and ). Additionally, and are positive definite matrices (often diagonal matrices of weights). This form of the GSVD is the core of certain techniques, such as generalized principal component analysis and Correspondence analysis.

The weighted form of the GSVD is called as such because, with the correct selection of weights, it generalizes many techniques (such as multidimensional scaling and linear discriminant analysis)[4]


The GSVD has been successfully applied to signal processing and big data, e.g., in genomic signal processing.[5][6][7] These applications also inspired a higher-order GSVD (HO GSVD)[8] and a tensor GSVD.[9]

See also[edit]

  • Paige, C. C.; Saunders, M. A. (1981). "Towards a Generalized Singular Value Decomposition". SIAM J. Numer. Anal. 18 (3): 398–405. doi:10.1137/0718026.
  • Gene Golub, and Charles Van Loan, Matrix Computations, Third Edition, Johns Hopkins University Press, Baltimore, 1996, ISBN 0-8018-5414-8
  • Hansen, Per Christian, Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion, SIAM Monographs on Mathematical Modeling and Computation 4. ISBN 0-89871-403-6
  • LAPACK manual [1]


  1. ^ Jolliffe I.T. Principal Component Analysis, Series: Springer Series in Statistics, 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. ISBN 978-0-387-95442-4
  2. ^ Greenacre, Michael (1983). Theory and Applications of Correspondence Analysis. London: Academic Press. ISBN 978-0-12-299050-2.
  3. ^ Abdi. H.; Williams, L.J. (2010). "Principal component analysis". Wiley Interdisciplinary Reviews: Computational Statistics. 2 (4): 433–459. doi:10.1002/wics.101.
  4. ^ Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 907-912.
  5. ^ O. Alter; P. O. Brown; D. Botstein (March 2003). "Generalized Singular Value Decomposition for Comparative Analysis of Genome-Scale Expression Datasets of Two Different Organisms". PNAS. 100 (6): 3351–3356. doi:10.1073/pnas.0530258100. PMC 152296. PMID 12631705.
  6. ^ C. H. Lee*; B. O. Alpert*; P. Sankaranarayanan; O. Alter (January 2012). "GSVD Comparison of Patient-Matched Normal and Tumor aCGH Profiles Reveals Global Copy-Number Alterations Predicting Glioblastoma Multiforme Survival". PLOS ONE. 7 (1): e30098. doi:10.1371/journal.pone.0030098. PMC 3264559. PMID 22291905. Highlight.
  7. ^ K. A. Aiello; O. Alter (October 2016). "Platform-Independent Genome-Wide Pattern of DNA Copy-Number Alterations Predicting Astrocytoma Survival and Response to Treatment Revealed by the GSVD Formulated as a Comparative Spectral Decomposition". PLOS ONE. 11 (10): e0164546. doi:10.1371/journal.pone.0164546. PMC 5087864. PMID 27798635.
  8. ^ S. P. Ponnapalli; M. A. Saunders; C. F. Van Loan; O. Alter (December 2011). "A Higher-Order Generalized Singular Value Decomposition for Comparison of Global mRNA Expression from Multiple Organisms". PLOS ONE. 6 (12): e28072. doi:10.1371/journal.pone.0028072. PMC 3245232. PMID 22216090. Highlight.
  9. ^ P. Sankaranarayanan*; T. E. Schomay*; K. A. Aiello; O. Alter (April 2015). "Tensor GSVD of Patient- and Platform-Matched Tumor and Normal DNA Copy-Number Profiles Uncovers Chromosome Arm-Wide Patterns of Tumor-Exclusive Platform-Consistent Alterations Encoding for Cell Transformation and Predicting Ovarian Cancer Survival". PLOS ONE. 10 (4): e0121396. doi:10.1371/journal.pone.0121396. PMC 4398562. PMID 25875127. AAAS EurekAlert! Press Release and NAE Podcast Feature.