AnchorBlues
commented
5 days ago @Sandy4321 Thank you for your question about handling unknown group structures in group lasso. This is a common challenge when applying group lasso to real-world datasets. Here are a few approaches you can consider:
- Domain knowledge: If possible, consult with domain experts to identify natural groupings in your data based on the features' meanings or relationships.
- Correlation-based grouping: You can use correlation analysis to group highly correlated features together. This can be done by setting a correlation threshold and clustering features that exceed this threshold.
- Hierarchical clustering: Apply hierarchical clustering to your features and cut the dendrogram at a certain level to create groups.
- Random grouping with stability selection: Randomly assign features to groups multiple times, run the group lasso for each grouping, and use stability selection to identify consistently selected features across different random groupings.
- Overlapping group lasso: Consider using overlapping group lasso, which allows features to belong to multiple groups, potentially mitigating the impact of incorrect groupings.
- Adaptive group lasso: Start with an initial grouping (e.g., each feature in its own group) and adaptively update the grouping structure based on the lasso results.
You can implement these approaches using the existing functions in our library combined with other Python data analysis tools.
may you help understand what to do if I do not know how many groups there in data?