2016
Song, Yang; Schreier, Peter J; Ramírez, David; Hasija, Tanuj
Canonical Correlation Analysis of High-Dimensional Data With Very Small Sample Support Artículo de revista
En: Signal Processing, vol. 128, pp. 449–458, 2016, ISSN: 01651684.
Resumen | Enlaces | BibTeX | Etiquetas: Bartlett-Lawley statistic, Canonical correlation analysis, Journal, Model-order selection, Principal component analysis, Small sample support
@article{Song2016,
title = {Canonical Correlation Analysis of High-Dimensional Data With Very Small Sample Support},
author = {Yang Song and Peter J Schreier and David Ram\'{i}rez and Tanuj Hasija},
url = {http://www.sciencedirect.com/science/article/pii/S0165168416300834},
doi = {10.1016/j.sigpro.2016.05.020},
issn = {01651684},
year = {2016},
date = {2016-11-01},
journal = {Signal Processing},
volume = {128},
pages = {449--458},
abstract = {This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett\textendashLawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise.},
keywords = {Bartlett-Lawley statistic, Canonical correlation analysis, Journal, Model-order selection, Principal component analysis, Small sample support},
pubstate = {published},
tppubtype = {article}
}
This paper is concerned with the analysis of correlation between two high-dimensional data sets when there are only few correlated signal components but the number of samples is very small, possibly much smaller than the dimensions of the data. In such a scenario, a principal component analysis (PCA) rank-reduction preprocessing step is commonly performed before applying canonical correlation analysis (CCA). We present simple, yet very effective, approaches to the joint model-order selection of the number of dimensions that should be retained through the PCA step and the number of correlated signals. These approaches are based on reduced-rank versions of the Bartlett–Lawley hypothesis test and the minimum description length information-theoretic criterion. Simulation results show that the techniques perform well for very small sample sizes even in colored noise.