🤖 AI Summary
This work addresses the lack of dedicated Python tools for Invariant Coordinate Selection (ICS) in the current machine learning ecosystem, which has hindered its adoption in tasks such as clustering and anomaly detection. We propose a dimensionality reduction method based on the joint diagonalization of two scatter matrices, effectively extracting features by optimizing directions corresponding to extremal generalized kurtosis. To enhance accessibility and reproducibility, we introduce ICSpyLab—the first open-source implementation of ICS under the MIT license—featuring support for multiple scatter matrix types, joint diagonalization algorithms, and component selection criteria. The library strictly adheres to the scikit-learn estimator interface and is accompanied by comprehensive documentation and reproducible examples, significantly improving the usability and extensibility of ICS methods.
📝 Abstract
Invariant coordinate selection (ICS) is a dimensionality reduction technique based on the joint diagonalization of two scatter matrices. While principal component analysis relies solely on variance, ICS seeks directions of maximal or minimal generalized kurtosis, making it a powerful alternative for clustering and anomaly detection. Despite its theoretical and practical relevance, no dedicated Python implementation of ICS is currently available, limiting its integration into modern machine-learning workflows. We introduce ICSpyLab, the first Python package implementing ICS. It provides a broad collection of scatter matrices, multiple algorithms for computing invariant components, and several component-selection criteria. Designed for both practitioners and researchers, ICSpyLab follows a standard estimator interface, facilitating its use within machine-learning pipelines while remaining flexible for methodological extensions. The documentation includes detailed explanations and reproducible examples. ICSpyLab is released under the MIT license and is openly available at: https://github.com/cbecquart/ICSpyLab.