The thresholded feature has recently emerged as an extremely efficient, yet rough empirical
approximation, of the time-consuming sparse coding inference process. Such an
approximation has not yet been rigorously examined, and standard dictionaries often lead to
non-optimal performance when used for computing thresholded features. In this paper, we
first present two theoretical recovery guarantees for the thresholded feature to exactly
recover the nonzero support of the sparse code. Motivated by them, we then formulate the …