1. A method, comprising: receiving an input signal for classification and a set of pre-classified signals, wherein the input signal and each signal in the set of pre-classified signals comprises a vector representation of an object having a plurality of vector elements; determining a sparse vector comprising a plurality of sparse vector coefficients, each sparse vector coefficient corresponding to a signal in the set of pre-classified signals and representing a likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal of the set of pre-classified signals, and comparing a largest sparse vector coefficient of the determined sparse vector with a predetermined threshold; wherein, if the largest sparse vector coefficient is less than the predetermined threshold, the method further comprises: removing the signal of the set of pre-classified signals corresponding to the largest sparse vector coefficient from the set of pre-classified signals to produce a reduced set of pre-classified signals, and repeating, iteratively, the determining and comparing using the input signal and the reduced set of pre-classified signals, wherein each iteration of the input signal further comprises: determining whether there has been a previous iteration for the input signal; if there has been a previous iteration for the input signal, determining whether the largest sparse vector coefficient in a current iteration is less than the largest sparse vector coefficient in a previous iteration for the input signal, and if the largest sparse vector coefficient in the current iteration is less than the largest sparse vector coefficient in a previous iteration, providing a classification output based on the signal of the set of pre-classified signals corresponding to the largest sparse vector coefficient in the previous iteration.