New method enhances graph construction for machine learning tasks

Researchers have developed a novel method for constructing graphs in kernelized graph methods, specifically addressing the challenge of selecting an appropriate Gaussian bandwidth (sigma). The proposed approach uses a per-node bandwidth criterion that aligns the kernel's effective rank with the local intrinsic dimension, estimated via a minimum spanning tree. This adaptive bandwidth control aims to improve spectral complexity consistency with the underlying manifold. Evaluations on CIFAR-100 using SSL embeddings demonstrated that this adaptive bandwidth method consistently enhances accuracy in leave-one-out classification and label propagation compared to fixed-bandwidth techniques and other adaptive methods. AI

IMPACT This adaptive bandwidth control method could improve the performance of graph-based machine learning algorithms by more accurately capturing the underlying data manifold.

RANK_REASON The cluster contains a research paper detailing a new method for graph construction in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• CIFAR-100
• Diffusion map
• label propagation (LP)
• leave-one-out (LOO) classification
• minimum spanning tree
• sparse kernel regression graphs
• spectral clustering
• SSL embeddings

• paper
• other

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