Sublinear time classification via feature padding and hashing

A computer-implemented method, comprising:

• receiving a video vector that represents an input digital video;
• obtaining a plurality of classifier vectors, each classifier vector corresponding to at least one class;
• generating, by one or more computers, a padded video vector;
• generating, by the one or more computers, a plurality of padded classifier vectors comprising each of the plurality of classifier vectors padded with a scalar corresponding to the classifier vector;
• and
• classifying, by the one or more computers, the input digital video as including an object from at least one of the classes corresponding to the classifier vectors by approximating a dot product between the padded video vector and at least one of the plurality of padded classifier vectors.

A computer-implemented method, comprising:

• receiving an audio vector that represents an input digital audio;
• obtaining a plurality of classifier vectors, each classifier vector corresponding to at least one audio type;
• generating, by one or more computers, a padded audio vector;
• generating, by the one or more computers, a plurality of padded classifier vectors comprising each of the plurality of classifier vectors padded with a scalar corresponding to the classifier vector;
• and
• classifying, by the one or more computers, the input digital audio as including an audio type from at least one of the audio types corresponding to the classifier vectors by approximating a dot product between the padded audio vector and at least one of the plurality of padded classifier vectors.