Song, YangSchreier, Peter J.Ramírez García, DavidHasija, Tanuj2020-11-242020-11-242016-11-01Song, Y., Schreier, P. J., Ramírez, D., & Hasija, T. (2016). Canonical correlation analysis of high-dimensional data with very small sample support. Signal Processing, 128, 449-4580165-1684https://hdl.handle.net/10016/31469This 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. (C) 2016 Elsevier B.V. All rights reserved.eng© Elsevier, 2016Atribución-NoComercial-SinDerivadas 3.0 EspañaBartlett-Lawley statisticCanonical correlation analysisModel-order selectionPrincipal component analysisSmall sample supportInformation-theoretic criteriaSignalsNoiseNumberComponentsresearch articleTelecomunicacioneshttps://doi.org/10.1016/j.sigpro.2016.05.020open access449458Signal Processing128AR/0000018112