Singular value decomposition and principal component analysis

By Michael E. Wall, Andreas Rechtsteiner, Luis Mateus Rocha. 2003

1,430 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b894d837d4d0aac071c777914acfe9bc1a2f93c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Novel Unsupervised Feature Filtering of Biological Data (2006, methodology): “In PCA (or SVD) studies it is conventional to regard the best representation as the minimal least-square approximation of the original matrix ( Wall et al., 2003 ).”
Higher Order Dynamic Mode Decomposition (2017, background): “5), taking advantage of the well known [57] error-filtering ability of POD/SVD.”
A review on machine learning principles for multi-view biological data integration (2016, methodology): “Matrix factorization methods, such as principal component analysis [95, 96], factor analysis (FA) [97, 98], NMF [99, 100, 101, 102], SR [65] and tensor decomposition methods [103, 104], are commonly used feature extraction models.”
Handbook of Big Data (2016, methodology): “Furthermore, the statistical procedure principal component analysis has a direct relationship to the singular value decomposition (SVD) [16,26,31].”
The singular values and vectors of low rank perturbations of large rectangular random matrices (2011, background): “This theoretical justification along with the fact that these vectors can be efficiently computed using now-standard numerical algorithms for the SVD [28] has led to the ubiquity of the SVD in applications such as array processing [51], genomics [1,52], wireless communications [25], information retr”
Feature dimensionality reduction: a review (2022, methodology): “It is the generalization of the Eigen decomposition of a positive semideﬁnite normal matrix (for example, symmetric matrix with positive eigenvalues) to any m × n matrix via an extension of polar decomposition [66].”
Multimodal fusion for multimedia analysis: a survey (2010, background): “The readers may refer to [ 134 ] for further details about these feature dimensionality reduction methods.”
Large-Scale Embedding Learning in Heterogeneous Event Data (2016, methodology): “On the other hand, some dimension reduction methods can be adapted for event data embedding, such as principal component analysis [28], singular value decomposition [28], and non-negative matrix factorization [29].”