Online Learning in Tensor Space
Yuan Cao, Sanjeev P. Khudanpur · 2014
We propose an online learning algorithm based on tensor-space models. A tensor-space model represents data in a compact way, and via rank-1 approximation the weight tensor can be made highly struc-tured, resulting in a significantly smaller number of free parameters to be estimated than in comparable vector-space models. This regularizes the model complexity and makes the tensor model highly effective in situations where a large feature set is de-fined but very limited resources are avail-able for training. We apply with the pro-posed algorithm to a parsing task, and show that even with very little training data the learning algorithm based on a ten-sor model performs well, and gives signif-icantly better results than standard learn-ing algorithms based on traditional vector-space models. 1