Dynamic time-alignment k-means kernel clustering for time sequence clustering
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a novel method to cluster sequences by embedding a non-linear time alignment kernel function into kernel k-means. The time-alignment operation embeds the sequential pattern in the kernel function, allowing kernel k-means to be used to classify entire sequences. The method is evaluated with over 9800 videos and features from the LIRIS annotated creative commons emotional database. Our results show that the method works well in classifying sequences based on their affective content, and performs better than other unsupervised methods for clustering time series. In addition, this paper evaluates several methods abilities to map low-level features onto the valence arousal plane from the LIRIS database. The regression results also show that simple Ridge Regression had comparable performance to state-of-the-art regression methods.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it