I would like to know if it is useful (or maybe dangerous) to reduce the number of attributes (by selecting the most informative ones among thousands) before seeking for latent variables or not (in an exploratory perspective).

A subsidiary question: in the same case, would it be beneficial to select the most important features for each categorie of features (these can be compressed using an entity-attribute-value model which is not really suitable for data mining) before detecting the latent variables?