Oja Medians and Centers of Mass
Bibliographic record
Abstract
The bound in (1) is not known to be tight. The bound in (2) is tight, up to a lower-order term, for some point sets S. ∗This work was initiated during the Workshop on Computational Geometry 2006 in Caldes de Malavella. The authors wish to thank Ferran Hurtado and the organizers for the opportunity of working on this topic. †School of Computer Science, Carleton University, dchen4@connect.carleton.ca, morin@scs.carleton.ca ‡INRIA Sophia Antipolis Mediterranee, France. Olivier.Devillers@sophia.inria.fr §Department of Computer and Information Science, Polytechnic University, jiacono@poly.edu ¶Departement d’Informatique, Universite Libre de Bruxelles, stefan.langerman@ulb.ac.be 1In Oja’s original definition, the sum is normalized by dividing by (|S| d ) . We omit this here since it changes none of our results and clutters our formulas. 1.2 Related Results
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".