A review of machine learning with small and limited data

Abstract The abundance of large datasets has driven machine learning (ML) model performance and scalability breakthroughs. However, many domains and practical applications must contend with the limitations imposed by small and very small datasets. This survey thoroughly examines state-of-the-art methodologies and challenges in ML approaches tailored for scenarios where data scarcity is a fundamental constraint. We begin by outlining the theoretical foundations that govern learning from small data. Then, we discuss recent advancements in data-related frameworks (i.e., training and evaluation methods, etc.) and algorithmic architectures (meta and transfer learning). We also explore the trade-offs and related…

• CU
  Clemson University