A Semi-Supervised Approach for the Semantic Segmentation of Trajectories
Bibliographic record
Abstract
A first fundamental step in the process of analyzing movement data is trajectory segmentation, i.e., splitting trajectories into homogeneous segments based on some criteria. Although trajectory segmentation has been the object of several approaches in the last decade, a proposal based on a semi-supervised approach remains inexistent. A semi-supervised approach means that a user labels manually a small set of trajectories with meaningful segments and, from this set, the method infers in an unsupervised way the segments of the remaining trajectories. The main advantage of this method compared to pure supervised ones is that it reduces the human effort to label the number of trajectories. In this work, we propose the use of the Minimum Description Length (MDL) principle to measure homogeneity inside segments. We also introduce the Reactive Greedy Randomized Adaptive Search Procedure for semantic Semi-supervised Trajectory Segmentation (RGRASP-SemTS) algorithm that segments trajectories by combining a limited user labeling phase with a low number of input parameters and no predefined segmenting criteria. The approach and the algorithm are presented in detail throughout the paper, and the experiments are carried out on two real-world datasets. The evaluation tests prove how our approach outperforms state-of-the-art competitors when compared to ground truth.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".