Bibliographic record
Abstract
In the space of high-dimensional data, it is generally reasonable to assume that the data points are on (or close to) one or more submanifolds. Each of these submanifolds can be modeled by a number of linear subspaces. This is in fact the main intuition behind a majority of subspace clustering algorithms. In many cases, however, the subspaces computed by these algorithms consist of disconnected subsets of the underlying submanifolds and therefore, do not form localized and compact clusters. To address this problem, we propose "Low Dimensional Localized Clustering (LDLC)", a new method for subspace clustering. Unlike existing methods, LDLC respects the topology of the underling submanifolds and assigns the data points to localized clusters such that the total reconstruction error is minimized. This is a valuable property in many tasks, such as semi-supervised classification, data visualization and local dimensionality reduction. We establish connections between LDLC, K-Means, and VQPCA from different perspectives, and validate our method through various experiments on synthetic and real data sets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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 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".