  Representation learning has become a crucial area of research in machine
learning, as it aims to discover efficient ways of representing raw data with
useful features to increase the effectiveness, scope and applicability of
downstream tasks such as classification and prediction. In this paper, we
propose a novel method to generate representations for time series-type data.
This method relies on ideas from theoretical physics to construct a compact
representation in a data-driven way, and it can capture both the underlying
structure of the data and task-specific information while still remaining
intuitive, interpretable and verifiable. This novel methodology aims to
identify linear laws that can effectively capture a shared characteristic among
samples belonging to a specific class. By subsequently utilizing these laws to
generate a classifier-agnostic representation in a forward manner, they become
applicable in a generalized setting. We demonstrate the effectiveness of our
approach on the task of ECG signal classification, achieving state-of-the-art
performance.
