Modern graphical tools have enhanced our ability to learn many
things from data directly, but the issue is what to plot with a
high-dimensional data set. Thus dimension reduction shows its
importance. Based on information theory, here we develop a model-free dimension reduction technique for extracting important information. Our method in general could be applied in any area relating to information extraction. It also can be viewed as inverse regression. It involves density estimation which can be estimated non-parametrically. And that is feasible with the help of fast computing techniques. We provide theoretical justification for connecting with central
subspace. Illustrative examples are presented.
