Fast and memory-optimal dimension reduction using Kac’s walk
Bibliographic record
Abstract
In this work, we analyze dimension reduction algorithms based on the Kac walk and discrete variants. (1) For n points in Rd, we design an optimal Johnson–Lindenstrauss (JL) transform based on the Kac walk which can be applied to any vector in time O(dlogd) for essentially the same restriction on n as in the best-known transforms due to Ailon and Liberty, and Bamberger and Krahmer. Our algorithm is memory-optimal, and outperforms existing algorithms in regimes when n is sufficiently large and the distortion parameter is sufficiently small. In particular, this confirms a conjecture of Ailon and Chazelle, and of Oliveira, in a stronger form. (2) The same construction gives a simple transform with optimal restricted isometry property (RIP) which can be applied in time O(dlogd) for essentially the same range of sparsity as in the best-known such transform due to Ailon and Rauhut. (3) We show that by fixing the angle in the Kac walk to be π/4 throughout, one obtains optimal JL and RIP transforms with almost the same running time, thereby confirming—up to a loglogd factor—a conjecture of Avron, Maymounkov, and Toledo. Our moment-based analysis of this modification of the Kac walk may also be of independent interest in connection with repeated averaging processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".