Blind detection of independent dynamic components

By Lars Kai Hansen, Jan Larsen, Thomas Kolenda. 2001

84 citations recorded by the source corpus.

How researchers see this paper

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

Revealing evolutionary constraints on proteins through sequence analysis (2018, methodology): “The associated eigenvectors accurately recover both ~ D 1 and ~ D 2 , after a final step of Independent Component Analysis (ICA) [7, 53, 54] that successfully disentangles the contributions coming from the two constraints (see S1 Appendix).”
Exploratory Factor Analysis With Small Sample Sizes (2009, methodology): “We made several attempts to estimate the correct number of factors in the first series of simulations by using Velicer's Minimum Average Partial ( O'Connor, 2000 ), a Bayesian Information Criterion ( Hansen, Larsen, & Kolenda, 2001 ), an automatic scree test ( Zhu & Ghodsi, 2006 ), and parallel anal”
Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size (2012, methodology): “There are a variety of methods that can assist in this decision, such as Velicer’s Minimum Average Partial [38], Bayesian Information Criterion [25], parallel analysis [38], and generalized degrees of freedom [10].”
Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. (2006, methodology): “In particular, ICAPCA and pICA performed very well, followed by FA, NMF and ICA, then PCA (Fig.”