SomHilbert
SomHilbert is a term used to describe a family of self-organizing map–like models that operate within a Hilbert space framework. It denotes the idea of mapping data into a (potentially infinite‑dimensional) Hilbert space in order to exploit the geometry of inner products, while preserving a topological structure similar to that of a traditional self‑organizing map (SOM). The concept appears in discussions that seek to combine topographic mapping with kernel methods or feature embeddings.
In somHilbert, data x belong to an input space X and are associated with a feature map
SomHilbert generalizes classical SOMs by allowing nonlinear representations through φ and by leveraging kernel methods. When φ is
Potential applications include pattern recognition on high‑dimensional or structured data, functional data analysis, and scenarios where