For component analysis, fsml currently provides only pca (and eof), i.e. the diagonalization of the correlation matrix $C_{xx}$. Another very common tool is Canonical Correlation Analysis. Given two random vector variables $x$ and $y$, it essentially amounts to simultaneously diagonalize $C_{xx}$, $C_{yy}$ and $C_{xy}$ and does so by solving the following eigenvalue problems
$$
\lambda p = C_{xx}^{-\dfrac12} C_{xy} C_{yy}^{-1} C_{yx} C_{xx}^{-\dfrac12}
$$
and
$$
\lambda q = C_{yy}^{-\dfrac12} C_{yx} C_{xx}^{-1} C_{xy} C_{yy}^{-\dfrac12}.
$$
Then $p_i^\top C_xx p_j = \delta_{ij}$, $q_i^\top C_yy q_j = \delta_{ij}$ and $p_i^\top C_{xy} q_j = \lambda_i \delta_{ij}$. Note that if $x$ and $y$ are Gaussian random vectors, the $\lambda$'s are related to the mutual information between the two random variables.
Would the addition of such a component analysis technique be of interest to you?
For component analysis,$C_{xx}$ . Another very common tool is Canonical Correlation Analysis. Given two random vector variables $x$ and $y$ , it essentially amounts to simultaneously diagonalize $C_{xx}$ , $C_{yy}$ and $C_{xy}$ and does so by solving the following eigenvalue problems
fsmlcurrently provides onlypca(andeof), i.e. the diagonalization of the correlation matrixand
Then$p_i^\top C_xx p_j = \delta_{ij}$ , $q_i^\top C_yy q_j = \delta_{ij}$ and $p_i^\top C_{xy} q_j = \lambda_i \delta_{ij}$ . Note that if $x$ and $y$ are Gaussian random vectors, the $\lambda$ 's are related to the mutual information between the two random variables.
Would the addition of such a component analysis technique be of interest to you?