Research map / Learning theory and algorithms / Matrix and tensor methods
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use iSearch innovation-direction values instead of coherence (CoP) to detect misclassified points | robust PCA · Hopkins155 (3 clusters, 27 matrices) | clustering error after robust PCA: 7 % -> 2 % | Helped |
| use iSearch instead of FMS/R1-PCA/PCA | robust PCA · Hopkins155 (3 clusters, 27 matrices) | clustering error after robust PCA: 20.3 % -> 2 % (FMS) | Helped |
| use iSearch instead of R1-PCA on Waving Tree frames | outlier frame detection · Waving Tree video | outlying frames detected | Helped |
Closest papers by meaning
- Accelerated Combinatorial Search for Outlier Detection with Provable Bound on Sub-Optimality (AAAI 2021 · similarity 0.92)
- Residual-Based Sampling for Online Outlier-Robust PCA (ICML 2022 · similarity 0.87)
- Refined Complexity of PCA with Outliers (ICML 2019 · similarity 0.87)
- Fixed-Parameter and Approximation Algorithms for PCA with Outliers (ICML 2021 · similarity 0.87)
- A Pseudo-Bayesian Algorithm for Robust PCA (NeurIPS 2016 · similarity 0.87)
- Capturing the denoising effect of PCA via compression ratio (NeurIPS 2024 · similarity 0.86)
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