PrimalCCA

An implementation of primal regularised Canonical Correlation Analysis (CCA) algorithm.

Methods

class apgl.features.PrimalCCA(tau)

Intialise the object with regularisation parameter tau between 0 (no regularisation) and 1 (full regularisation).

learnModel(X, Y)

Learn the CCA directions using set of examples given the numpy.ndarrays X and Y. These matrices have rows as their example, and must have the same number of rows.

project(testX, testY, k=None)

Project the examples in the CCA subspace using set of test examples testX and testY. The number of projection directions is specified with k, and if this parameter is None then all directions are used.