Necessary and sufficient conditions for high-dimensional salient feature subset recovery

We consider recovering the salient feature subset for distinguishing between two probability models from i.i.d. samples. Identifying the salient set improves discrimination performance and reduces complexity. The focus in this work is on the high-dimensional regime where the number of variables d, the number of salient variables k and the number of samples n all grow. The definition of saliency is motivated by error exponents in a binary hypothesis test and is stated in terms of relative entropies. It is shown that if n grows faster than max{ck log((d-k)/k), exp(c´k)} for constants c, c´, then the error probability in selecting the salient set can be made arbitrarily small. Thus, n can be much smaller than d. The exponential rate of decay and converse theorems are also provided. An efficient and consistent algorithm is proposed when the distributions are graphical models which are Markov on trees.