Online Learning in Tensor Space

We propose an online learning algorithm based on tensor-space models. A tensorspace model represents data in a compact way, and via rank-1 approximation the weight tensor can be made highly structured, 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 defined but very limited resources are available for training. We apply with the proposed algorithm to a parsing task, and show that even with very little training data the learning algorithm based on a tensor model performs well, and gives significantly better results than standard learning algorithms based on traditional vectorspace models.