a-TMFG: Scalable Triangulated Maximally Filtered Graphs via Approximate Nearest Neighbors
The traditional Triangular Maximally Filtered Graph (TMFG) construction requires pre-computation and storage of a dense correlation matrix; this limits its applicability to small and medium-sized datasets. Here we identify key memory and runtime complexity challenges when using TMFG at scale. We then present the Approximate Triangular Maximally Filtered Graph (a-TMFG) algorithm. This is a novel approach to scaling the construction of artificial graphs from data inspired by TMFG. The method employs k-Nearest Neighbors Graphs (kNNG) for initial construction, and implements a memory management strategy to search and estimate missing correlations on-the-fly. This provides representations to c...
| Authors | Lionel Yelibi |
|---|---|
| Primary category | stat.ML |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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