1. A computer-implemented method for recognition of high-dimensional data in the presence of occlusion, comprising: receiving by a computer a target data that includes an occlusion and is of an unknown class, wherein the target data comprises a known object; sampling with the computer a plurality of training data files comprising a plurality of distinct classes of the same object as that of the target data; and identifying the class of the target data by the computer through linear superposition of the sampled training data files using l 1 minimization, wherein a linear superposition with a sparsest number of coefficients is used to identify the class of the target data.