Regularized horseshoe prior for latent factor-loadings matrix? (in latent factor analysis)

Hi, I found there were various discussions on prior selections like this, but I could not find a clear answer about it, especially related to horseshoe prior for latent factor analysis.

The main reason that I am looking for this is to find a substitute for Bhattacharya & D. B. Dunson, 2011 to implement in Stan.

Piironen & Vehtari, 2017 was very helpful, but I got stuck expanding the regularized horseshoe prior to a prior for factor-loadings matrix.

For example, a simple latent factor model can be described as

\mathbf{y_i} = \mathbf{ \Lambda} \mathbf{\eta_i} + \mathbf{\epsilon_i}, \: \mathbf{\epsilon_i} \sim N_p(0, \mathbf{\Sigma})

where \mathbf{y_i} =[y_{i1}, ..., y_{ip}]^T, \mathbf{\eta_i} is a vector with k latent factors, \mathbf{ \Lambda} is p x k factor loadings matrix, and \mathbf{\Sigma} = diag(\sigma_1^2, ..., \sigma_p^2).

My plan was to combine multiple vectors generated from horseshoe function (implemented in brms)

functions {
  vector horseshoe(vector zb, vector[] local, real[] global,
                   real scale_global, real c2) {
    int K = rows(zb);
    vector[K] lambda = local[1] .* sqrt(local[2]);
    vector[K] lambda2 = square(lambda);
    real tau = global[1] * sqrt(global[2]) * scale_global;
    vector[K] lambda_tilde = sqrt(c2 * lambda2 ./ (c2 + tau^2 * lambda2));
    return zb .* lambda_tilde * tau;

in a column-wise fashion like this (dimension of sigma is not appropriate here).

for (k in 1:K){
    factor_loading[:,k] = horseshoe(zb[k], hs_local1[k], hs_local2[k], hs_global1[k], hs_global2[k], hs_scale_global * sigma[k], hs_scale_slab^2 * hs_c2[k]);

I am wondering,
1. Is it valid to do so to generate a factor-loadings matrix?
2. In this case, how can I deal with sigma, which is now a vector, when calculating tau?

Thank you in advance for your comments.


I did not find a solution, and I abandonned the idea, I’m good with a limited number of factors. The thought I had about it :

By implementing such a prior, we remove the constraints on the loading matrix that make it identifiable (basically upper diag zero loadings and positive diagonal). I read some articles in which they relaxed a little bit these constraints, but I did not find these really interesting.

I have the strong feeling that pushing loadings toward zero in a unsupervized way would lead to non identifiable model : multiple loading matrix with different combinations of zero would be equivalent without constraints. In the Bhattacharya and Dunson paper, the higher the index of a loading column was, the higher it was shrinked.

Good success in your research!

Hi, @ldeschamps.
Thanks for the comment.
I was kind of hoping for your comment.

Then, what model did you end up with?
And did you try to implement horseshoe prior that is not regularized? If so, how did it perform?