1. A method, comprising: at a recognition system, receiving a sample; and assigning a label to the sample through employment of a computer-implemented deep tensor neural network, the deep tensor neural network comprising a plurality of layers, the plurality of layers comprising: a plurality of hidden layers that comprise a projection layer and a tensor layer, each hidden layer in the plurality of hidden layers at least one of: receives data output from another immediately adjacent hidden layer in the plurality of hidden layers; or outputs data to another immediately adjacent hidden layer in the plurality of hidden layers; the projection layer comprising a plurality of subspaces, each subspace in the plurality of subspaces comprising respective hidden units, and wherein each subspace is provided with a respective projection of input data from a first immediately adjacent layer in the deep tensor neural network, wherein projections provided to respective subspaces are nonlinear projections of the input data, and wherein the tensor layer receives respective output data from each subspace in the plurality of subspaces and generates an output vector for provision to a second immediately adjacent layer in the deep tensor neural network, the output vector being based at least in part upon the respective output data from subspaces in the projection layer.