A tensor-space model is a compact representation of data, and via rank-1 tensor approximation, the weight tensor can be made highly structured hence the number of parameters to be trained is significantly reduced.
this can be regarded as a form of model regularization.compared with the traditional vector-space models, learning in the tensor space is very effective when a large feature set is defined, but only small amount of training data is available.