(Pro version only)
The Stochastic Subspace Identification techniques all uses the same estimation engine for estimation of state space realizations (models). In general, the input to this engine is a weighted version of the so-called Common SSI Input matrix that consist of compressed time series data. The difference between the three Stochastic Subspace Identification techniques is how this matrix is weighted.
The Stochastic Subspace Identification editor initialized for state space estimation using the Canonical Variate Analysis algorithm either from the SSI Pane of the Task Bar by pressing the button called CVA Estimation or from the Project, Modal Analysis, Stochastic Subspace Identification, Canonical Variate Analysis menu item. This algorithm typically forces the use of a larger state space dimension that the two other available algorithms. The reason is its ability to estimate modes with a large difference in energy level. In order to see low excited modes among well-excited modes, it is necessary to force a large state space dimension. If you have data only with well-excited modes use e.g. the Unweighted Principal Component algorithm instead as your first choice.
See the Technical Paper on the Stochastic Subspace Identification Techniques for a more comprehensive description about how the Stochastic Subspace Identification techniques works and the specific mathematical formulation of the Canonical Variate Analysis algorithm.