Unsupervised Feature Selection Using Orthogonally Constrained Matrix Factorization with Hessian Regularization and Non-Convex Sparsity
Bibliographic record
Abstract
Unsupervised feature selection plays a crucial role in handling high-dimensional data, especially in scenarios where labels are unavailable. This study introduces a novel feature selection method that integrates orthogonally constrained matrix factorization, Hessian regularization, and non-convex sparsity -- l_(2,1-2)-norm regularization. The proposed method captures the local geometrical of the data while keeping it easy to interpret and sparse. Using the l_(2,1-2)-norm, it highlights important features, removes unnecessary information, and makes the model more resistant to noise and outliers. The use of Hessian regularization preserves the intrinsic manifold structure, and orthogonal constraints promote independence among latent components. Experimental results on multiple datasets demonstrate the effectiveness of the proposed method, achieving competitive performance in clustering accuracy and normalized mutual information (NMI), even with significantly reduced feature dimensions. This study emphasizes the potential of the proposed framework for unsupervised learning tasks involving high-dimensional, unlabeled data.
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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 | 0.001 |
| 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".