Bibliographic record
Abstract
Clustering is a commonly used technique for multimedia data analysis and management. In this article, we propose a high-order clustering algorithm by fast search and find of density peaks (HOCFS) by extending the traditional clustering scheme by fast search and find of density peaks (CFS) algorithm from the vector space to the tensor space for multimedia data clustering. Furthermore, we propose a privacy preserving HOCFS algorithm (PPHOCFS) which improves the efficiency of the HOCFS algorithm by using the cloud computing to perform most of the clustering operations. To protect the private data in the multimedia data sets during the clustering process on the cloud, the raw data is encrypted by the Brakerski-Gentry-Vaikun-tanathan (BGV) strategy before being uploaded to the cloud for performing the HOCFS clustering algorithm efficiently. In the proposed method, the client is required to only execute the encryption/decryption operations and the cloud servers are employed to perform all the computing operations. Finally, the performance of our scheme is evaluated on two representative multimedia data sets, namely NUS-WIDE and SNAE2, in terms of clustering accuracy, execution time, and speedup in the experiments. The results demonstrate that the proposed PPHOCFS scheme can save at least 40% running time compared with HOCFS, without disclosing the private data on the cloud, making our scheme securely suitable for multimedia big data clustering.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".