Latent Variable Dimensionality Reduction Using a Kullback-Leibler Criterion and Its Application to Predict Antidepressant Treatment Response
Bibliographic record
Abstract
In this paper, we propose a method for dimensionality reduction of high-dimensional input data in a binary classification problem. The method is based on selecting a few latent variables that maximize the Kullback-Leibler (KL) distance between the two class distributions, under the assumption that these distributions are multivariate Gaussian. Numerical performance is demonstrated by solving the challenging problem of classifying patients with major depressive disorder (MDD) into responders vs. non-responders to an anti-depressant treatment using pre-treatment resting electroencephalography (EEG) data. The extracted feature set measures consistent connectivity and includes the magnitude coherence features among all electrode pairs in a 3Hz to 30Hz bandwidth with 1Hz resolution. An overall 86% prediction performance indicates the effectiveness of the KLDR method in this application. This performance level was found to exceed that of other dimensionality reduction methods, namely the unsupervised principal component (PCA) and the supervised Fisher discriminant analysis (FDA) methods.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".