Learning K-Nearest Neighbour Regression for Noisy Dataset with Application in Indoor Localization
Why this work is in the frame
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Bibliographic record
Abstract
Many indoor location estimation algorithms compare the received signal strengths from WiFi access points to a prerecorded dataset, which may be composed of carefully mea-sured and curated data through a massive calibration campaign, and a large volume of crowd-sourced data captured by casual users. The crowd-sourced data usually contains valuable, but at the same time noisy, measurements where the noise might be in the features and/or the labels of data points. The treatment of such a dataset is challenging, as improper application of the dataset might result in inaccurate location estimation. In this paper, we propose a learning algorithm that makes the K-Nearest Neighbour (KNN) regression robust to noises both in features and labels of the training data. An intuition on why the learning algorithm should work is provided and the effectiveness of the algorithm is shown by experiments in a real environment.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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 it