Signature features with the visibility transformation
This work addresses pattern recognition tasks by providing a unified feature set, but it appears incremental as it builds on existing visibility transformation concepts.
The paper established a theoretical foundation for the visibility transformation, demonstrating its ability to embed absolute position effects into signature features efficiently, which simplifies pattern recognition by accommodating nonlinear functions of absolute and relative values.
In this paper we put the visibility transformation on a clear theoretical footing and show that this transform is able to embed the effect of the absolute position of the data stream into signature features in a unified and efficient way. The generated feature set is particularly useful in pattern recognition tasks, for its simplifying role in allowing the signature feature set to accommodate nonlinear functions of absolute and relative values.