Encoding large information structures in linear algebra and statistical models. (arXiv:2201.08233v3 [cs.LG] UPDATED)

Large information sizes in samples and features can be encoded to speed up
the learning of statistical models based on linear algebra and remove unwanted
signals. Encoding information can reduce both sample and feature dimension to a
smaller representational set. Here two examples are shown on linear mixed
models and mixture models speeding up the run time for parameter estimation by
a factor defined by the user's choice on dimension reduction (can be linear,
quadratic or beyond based on dimension …

arxiv encoding information lg linear linear algebra statistical