Research map / Learning theory and algorithms / Prediction and decision losses
QuantTree: Histograms for Change Detection in Multivariate Data Streams
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use QuantTree distribution-free thresholds (Algorithm 2) instead of asymptotic chi-square thresholds for the Pearson statistic | Pearson statistic on QuantTree histograms · Gaussian and real-world datasets, small TR (N=4096, nu=64) | FPR (target alpha=0.05) | Helped |
| Use QuantTree histograms with uniform-density bins (pi_k=1/K) instead of Voronoi, Density Tree partitions | Histogram-based change detection · Real-world datasets, power, K=32 and K=128 | power | Helped |
| Smaller number of bins K (32 vs 128) | QuantTree tests · Gaussian and real datasets, FPR control | FPR | Helped |
| Total variation statistic vs Pearson statistic on QuantTree histograms | QuantTree histograms · Gaussian datasets, FPR | FPR | Mixed |
| Increasing N and nu (large TR configuration) | QuantTree tests · Real and synthetic datasets, power | power | Helped |
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