Apple ML Research applies graph algorithms to UMAP's internal kNN graph

Apple Machine Learning Research has published a paper detailing how standard graph algorithms can be applied to the internal k-nearest-neighbor (kNN) graph constructed by Uniform Manifold Approximation and Projection (UMAP). This approach aims to enhance data sensemaking by leveraging the graph's representation of the data manifold before UMAP's projection distorts it. The research demonstrates that algorithms like PageRank, k-core decomposition, and clustering coefficient can effectively identify representative data points, dense regions, and tightly-knit neighborhoods, offering practical and competitive alternatives to existing methods for tasks such as exemplar selection and density-based clustering. AI

IMPACT Enhances data analysis techniques by leveraging internal graph structures within dimensionality reduction methods.

RANK_REASON Research paper published by Apple Machine Learning Research on applying graph algorithms to UMAP's internal kNN graph. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Apple Machine Learning Research →

• Apple Machine Learning Research
• clustering coefficient
• Dominik Moritz
• Donghao Ren
• Duen Horng (Polo) Chau
• Fashion-MNIST
• Fred Hohman
• HDBSCAN
• k-medoids
• kNN graph
• MNIST database
• PageRank
• Uniform Manifold Approximation and Projection

• paper
• other

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