Equation 8 · Embeddings and the Geometry of Similarity
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Read the formula alongside the article passage below. Each part has a deeper page with its role in the equation, the supporting passage and nearby citations.
This mathematical expression combines the displayed quantities; its precise role follows from the surrounding article text. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.
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Symbol L_k
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subscript
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What the article says around this equation
Aggarwal, Hinneburg and Keim then showed the rate depends on the norm. For uniform data under an metric, the expected gap between the farthest and nearest distances scales as
Sources cited in the article section
- [11] When Is "Nearest Neighbor" Meaningful? ↗
- [12] On the Surprising Behavior of Distance Metrics in High Dimensional Space ↗
- [13] Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data ↗
These citations give research context. Read each source to check which claims it supports.