Technologies for classification using sparse coding in real time

A method for classifying an input vector by a compute device, the method comprising:

• acquiring, by the compute device, (i) the input vector, (ii) a dictionary comprising a plurality of basis vectors, and (iii) one or more classifier parameters;
• determining, by the compute device and with use of a pattern-matching accelerator of the compute device, a plurality of sparse coding coefficients based on the dictionary and the input vector, wherein (i) each of the plurality of sparse coding coefficients is indicative of a magnitude of a corresponding basis vector of the plurality of basis vectors and (ii) the plurality of sparse coding coefficients define an approximation of the input vector, and wherein determining the plurality of sparse coding coefficients comprises determining, by the pattern-matching accelerator, an L1-norm distance from a residual vector to each of the plurality of basis vectors;
• and
• classifying, by the compute device, the input vector based on the plurality of sparse coding coefficients and the one or more classifier parameters.

One or more computer-readable storage media comprising a plurality of instructions stored thereon that, when executed, cause a compute device comprising a pattern-matching accelerator to:

• acquire (i) an input vector, (ii) a dictionary comprising a plurality of basis vectors, and (iii) one or more classifier parameters;
• determine, with use of the pattern-matching accelerator, a plurality of sparse coding coefficients based on the dictionary and the input vector, wherein (i) each of the plurality of sparse coding coefficients is indicative of a magnitude of a corresponding basis vector of the plurality of basis vectors and (ii) the plurality of sparse coding coefficients define an approximation of the input vector, and wherein to determine the plurality of sparse coding coefficients comprises to determine, by the pattern-matching accelerator, an L1-norm distance from a residual vector to each of the plurality of basis vectors;
• and
• to classify the input vector based on the plurality of sparse coding coefficients and the one or more classifier parameters.