Multi-view mid fusion: a universal approach for learning in an HDLSS setting
This work addresses the HDLSS problem in applications with limited samples, offering a foundational approach for further research, though it appears incremental as it adapts existing mid fusion techniques.
The paper tackled the challenge of learning in high-dimensional low-sample-size (HDLSS) settings by proposing a universal approach using multi-view mid fusion, showing that existing methods perform well even without inherent views, with experimental validation confirming effectiveness across various model-types and tasks.
The high-dimensional low-sample-size (HDLSS) setting presents significant challenges in various applications where the feature dimension far exceeds the number of available samples. This paper introduces a universal approach for learning in HDLSS setting using multi-view mid fusion techniques. It shows how existing mid fusion multi-view methods perform well in an HDLSS setting even if no inherent views are provided. Three view construction methods are proposed that split the high-dimensional feature vectors into smaller subsets, each representing a different view. Extensive experimental validation across model-types and learning tasks confirm the effectiveness and generalization of the approach. We believe the work in this paper lays the foundation for further research into the universal benefits of multi-view mid fusion learning.