Similarity-Based Representation Factorization: A General Method for Interpretable Dimensions
martin_hebart · x · 2026-09-25
A second NeurIPS 2026 paper from the same team introduces Similarity-Based Representation Factorization (SRF), a general computational method that recovers low-dimensional, non-negative, interpretable embeddings from similarity matrices.
- SRF recovers interpretable dimensions across simulations and many neural, behavioral, and computational datasets, even with sparse, incomplete data.
- The dimensions match task-specific models, predict independent behavioral properties, improve exploratory analysis, and offer higher power for confirmatory hypothesis testing than comparing similarity matrices.
- Positioned as a general-purpose tool for neuroscience, psychology, and AI representation research.
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