Welcome to cca-zoo’s documentation!¶
- Standard install
- Deep install
A variety of linear CCA and PLS methods implemented using alternating minimization methods for non-convex optimisation based on the power method or alternating least squares.
GCCA (Generalized MAXVAR CCA):
The generalized eigenvalue problem form of generalized MAXVAR CCA. Maximises the squared correlation between each view projection and a shared auxiliary vector of unit length.
MCCA (Multiset SUMCOR CCA):¶
The generalized eigenvalue problem form of multiset SUMCOR CCA. Maximises the pairwise sum of correlations between view projections.
SCCA (Sparse CCA - Mai):¶
A solution to the sparse CCA problem based on iterative rescaled lasso regression problems to ensure projections are unit length.
PMD (Sparse PLS/PMD/Penalized Matrix Decomposition - Witten):¶
A solution to a sparse CCA problem based on penalized matrix decomposition. The relaxation and assumptions made make this method more similar to an l1-regularized PLS
PCCA (Penalized CCA - elastic net - Waaijenborg):¶
A solution to the sparse CCA problem based on iterative rescaled elastic regression problems to ensure projections are unit length.
SCCA_ADMM (Sparse canonical correlation analysis-Suo):¶
A solution to the sparse CCA problem based on iterative rescaled lasso regression problems solved using ADMM.
CCA solved using the kernel method. Adding regularisation in the linear case can be shown to be equivalent to regularised CCA.
Linear Kernel RBF Kernel Polynomial Kernels
DCCA (Deep CCA):¶
Using either Andrew’s original Tracenorm Objective or Wang’s alternating least squares solution
DGCCA (Deep Generalized CCA):¶
An alternative objective based on the linear GCCA solution. Can be extended to more than 2 views
DMCCA (Deep Multiset CCA):¶
An alternative objective based on the linear MCCA solution. Can be extended to more than 2 views