Comparisons of Various ELM Based Multi-View Clustering Methods for WDSNs
Bibliographic record
Abstract
Unsupervised or semi-supervised learning algorithms are essential for solving practical clustering problems due to the real world data are mostly unlabeled or partially labeled. Some common challenges such as poor clustering accuracy and slow learning speed are still need to be solved. Recently, various extreme learning machine (ELM) based methods have been found in clustering due to its extremely fast learning speed and excellent approximation ability. In this paper, some brief introductions of general single-viewed (SV) and multi-viewed (MV) clustering algorithms are firstly included and then the investigation on combining ELM and MV algorithms are emphasized. Furthermore, combination of ELM and SV methods to solve MV clustering problems is attempted and satisfactory simulation results are obtained. Experiments are performed to compare the clustering performance in terms of effectiveness and efficiency among various of algorithms on a set of real world wireless distributed sensor networks (WDSNs) data. It is shown that the ELM based clustering algorithms achieve better clustering accuracy with less time consumption than conventional methods. Our attempts of the combining ELM and SV algorithms achieve comparable accuracy to ELM based MV algorithms but with even better time efficiency.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".