Researchers have developed a new framework for computing forest proximities, which are used to derive supervised similarities from decision trees. This framework, based on Separable Weighted Leaf-Collision (SWLC) kernels, unifies existing proximity methods by revealing a common sparse leaf-incidence structure. The approach allows for an explicit leaf-space representation and an exact sparse factorization of the proximity matrix, reducing computation to sparse linear algebra. Implemented in Python, the method achieves near-linear scaling in time and memory, enabling faster kernel computation and task-aware embedding. AI
IMPACT This research offers a more scalable method for kernel computation in machine learning, potentially enabling broader applications of decision forests in representation learning and kernel pipelines.
RANK_REASON This is a research paper published on arXiv detailing a new computational framework for machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
- Adrien Aumont
- alphaXiv
- arXiv
- CatalyzeX
- Connected Papers
- DagsHub
- Gotit.pub
- Hugging Face
- IArxiv
- Litmaps
- Python
- ScienceCast
- scite Smart Citations
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
