“Generalized singular value decomposition”的意思、由来-开放百科全书

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

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.

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

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]

Applications

The GSVD has been successfully applied to signal processing and big data, e.g., in genomic signal processing.^[5]^[6]^[7]

See also

References

1. ^Jolliffe I.T. [https://www.springer.com/west/home/new+%26+forthcoming+titles+%28default%29?SGWID=4-40356-22-2285433-0 Principal Component Analysis], Series: [https://www.springer.com/west/home/statistics/statistical+theory+and+methods?SGWID=4-10129-69-173621571-0 Springer Series in Statistics], 2nd ed., Springer, NY, 2002, XXIX, 487 p. 28 illus. {{ISBN|978-0-387-95442-4}}
2. ^{{Cite book | author = Greenacre, Michael | publisher=Academic Press |location= London | year = 1983 | title = Theory and Applications of Correspondence Analysis | isbn = 978-0-12-299050-2 }}
3. ^{{Cite journal|author1=Abdi. H. |author2=Williams, L.J. |year = 2010 | title = Principal component analysis. | journal = Wiley Interdisciplinary Reviews: Computational Statistics | volume = 2 |issue=4 | pages = 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. ^{{Cite journal |author1=O. Alter |author2=P. O. Brown |author3=D. Botstein | title = Generalized Singular Value Decomposition for Comparative Analysis of Genome-Scale Expression Datasets of Two Different Organisms | journal = PNAS | volume = 100 | issue = 6 | pages = 3351–3356 | date = March 2003 | doi = 10.1073/pnas.0530258100 |pmid=12631705 |pmc=152296}}
6. ^{{Cite journal |author1=C. H. Lee* |author2=B. O. Alpert* |author3=P. Sankaranarayanan |author4=O. Alter | title = GSVD Comparison of Patient-Matched Normal and Tumor aCGH Profiles Reveals Global Copy-Number Alterations Predicting Glioblastoma Multiforme Survival | journal = PLOS ONE | volume = 7 | issue = 1 | pages = e30098 | date = January 2012 | doi = 10.1371/journal.pone.0030098 | id = Highlight | pmid=22291905 | pmc=3264559}}
7. ^{{Cite journal |author1=K. A. Aiello |author2=O. Alter | title = 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 | journal = PLOS ONE | volume = 11 | issue = 10 | pages = e0164546 | date = October 2016 | doi = 10.1371/journal.pone.0164546 | pmid=27798635 | pmc=5087864}}
8. ^{{Cite journal |author1=S. P. Ponnapalli |author2=M. A. Saunders |author3=C. F. Van Loan |author4=O. Alter | title = A Higher-Order Generalized Singular Value Decomposition for Comparison of Global mRNA Expression from Multiple Organisms | journal = PLOS ONE | volume = 6 | issue = 12 | pages = e28072 | date = December 2011 | doi = 10.1371/journal.pone.0028072 | id = Highlight | pmid=22216090 | pmc=3245232}}
9. ^{{Cite journal |author1=P. Sankaranarayanan* |author2=T. E. Schomay* |author3=K. A. Aiello |author4=O. Alter | title = 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 | journal = PLOS ONE | volume = 10 | issue = 4 | pages = e0121396 | date = April 2015 | doi = 10.1371/journal.pone.0121396 | id = AAAS EurekAlert! Press Release and [https://www.nae.edu/Projects/20730/wtop/134897.aspx NAE Podcast Feature] | pmid=25875127 | pmc=4398562}}