Reprint: a randomized extrapolation based on principal components for data augmentation

Understand

Data scarcity and data imbalance have attracted a lot of attention in many fields.

• Data augmentation, explored as an effective approach to tackle them, can improve the robustness and efficiency of classification models by generating new samples.
• This paper presents REPRINT, a simple and effective hidden-space data augmentation method for imbalanced data classification.
• Given hidden-space representations of samples in each class, REPRINT extrapolates, in a randomized fashion, augmented examples for target class by using subspaces spanned by principal components to summarize distribution structure of both source and target class.