Eigenvector-based dimensionality reduction for human activity recognition and data classification
Bibliographic record
Abstract
In the context of appearance-based human motion compression, representation, and recognition, we have proposed a robust framework based on the eigenspace technique. First, the new appearance-based template matching approach which we named “Motion Intensity Image” for compressing a human motion video into a simple and concise, yet very expressive representation. Second, a learning strategy based on the eigenspace technique is employed for dimensionality reduction using each of PCA and FDA, while providing maximum data variance and maximum class separability, respectively. Third, a new compound eigenspace is introduced for multiple directed motion recognition that takes care also of the possible changes in scale. This method extracts two more features that are used to control the recognition process. A similarity measure, based on Euclidean distance, has been employed for matching dimensionally-reduced testing templates against a projected set of known motions templates. In the stream of nonlinear classification, we have introduced a new eigenvector-based recognition model, built upon the idea of the kernel technique. A practical study on the use of the kernel technique with 18 different functions has been carried out. We have shown in this study how crucial choosing the right kernel function is, for the success of the subsequent linear discrimination in the feature space for a particular problem. Second, building upon the theory of reproducing kernels, we have proposed a new robust nonparametric discriminant analysis approach with kernels. Our proposed technique can efficiently find a nonparametric kernel representation where linear discriminants can perform better. Data classification is achieved by integrating the linear version of the NDA with the kernel mapping. Based on the kernel trick, we have provided a new formulation for Fisher's criterion, defined in terms of the Gram matrix only.
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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 | 0.001 |
| 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".