Augmenting features by relative transformation for small data

摘要

Small data means that the number of samples in data is small. In such case, their features are often hand-crafted so that they may be insufficient and inconsistent. Currently some feature augmentation methods are proposed to solve the problem. They are mainly for the larger data composed of images and videos, instead of small data. In the case of small data, the human cognition such as cognitive relativity can be more beneficial to machine learning, as humans routinely classify objects according to both their individual features and their environments. The relative transformation is an efficient way to formalize the cognitive relativity and has been validated in improving the performance of the machine learning. Although relationships among categorical central vectors of small data can greatly improve the performance, they have not been considered as features in current methods. This paper uses the relative transformation to model these relationships and then proposes a new feature augmentation method. These relationships as features are learned automatically for each sample through the neural network, instead of determined by the predefined rules. As it only uses the information of small data itself, without requiring any additional knowledge, it can be applied to any data with features. Lots of experimental results on small data validate the proposed method.