Research map / Learning theory and algorithms / Kernels and regression theory
The Kernel Kalman Rule — Efficient Nonparametric Inference with Recursive Least Squares
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace kernel Bayes' rule with kernel Kalman rule | RKHS belief update · Gaussian expectation estimation | MSE of hidden state | Helped |
| replace kernel Bayes' rule (a) with kernel Kalman rule | RKHS belief update · 10 updates on 100 estimates | mean time (s): 1.2149 s -> 0.0813 s | Helped |
| learn conditional operators on 500 samples (subspace KKR) with 100 reference samples instead of 100 samples (ablation) | KKR · 10 updates on 100 estimates | mean time (s): 0.0813 s -> 0.1074 s | No clear effect |
| use KKF/subKKF instead of other nonparametric filters (KBF(a), KKF-CEO) | pendulum filtering · Pendulum | MSE | Helped |
| use subKKF instead of UKF | quad-link prediction · Quad-link 1-3 step prediction | mean Euclidean distance | Helped |
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