Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime
Tanuj Hasija, Yang Song, Peter J. Schreier, David Ramírez · 2016
This paper addresses the problem of detecting the number of signals correlated across multiple data sets with small sample support. While there have been studies involving two data sets, the problem with more than two data sets has been less explored. In this work, a rank-reduced hypothesis test for more than two data sets is presented for scenarios where the number of samples is small compared to the dimensions of the data sets.