We demonstrate and develop dyadic–probabilistic methods in connection with non-homogeneous bilinear operators, namely singular integrals square functions. the full theory of using a modern point view. The main result is new global T</mml:mi...

Principal component analysis (PCA) is one of the most popular tools in multivariate exploratory data analysis. Its probabilistic version (PPCA) based on maximum likelihood procedure provides a manner to implement dimension reduction. Recently, bilinear PPCA (BPPCA) model, which assumes that noise terms follow matrix variate Gaussian distributions, has been introduced directly deal with two-dime...