Structured Learning on Mapper Representations
Modern machine learning (ML) methods are highly effective for prediction tasks, but many commonly used representations reduce complex data to fixed dimensional embeddings that may suppress multiscale structural organization. The Mapper algorithm from topological data analysis (TDA) provides a different perspective by decomposing data into overlapping local regions connected through a nerve construction, producing a structured representation that captures geometric organization, local statistical behavior, and relational connectivity simultaneously. In this work, we develop a framework for learning over Mapper induced structured representations. Rather than treating Mapper as a preprocessi...
| Authors | George Babus, Farzana Nasrin |
|---|---|
| Primary category | stat.ML |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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