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Convergence Analysis of Blurring Mean Shift
Blurring mean shift (BMS) algorithm, a variant of the mean shift algorithm, is a kernel-based iterative method for data clustering, where data points are clustered according to their convergent points via iterative blurring.
Label Smoothing is Robustification against Model Misspecification
For example, in binary classification, instead of the one-hot target $(1, 0)^\top$ used in conventional logistic regression (LR), LR with LS (LSLR) uses the smoothed target $(1-\frac{\alpha}{2},\frac{\alpha}{2})^\top$ with a smoothing level $\alpha\in(0, 1)$, which causes squeezing of values of the logit.
Convergence Analysis of Mean Shift
The mean shift (MS) algorithm seeks a mode of the kernel density estimate (KDE).
Optimal Kernel for Kernel-Based Modal Statistical Methods
Kernel-based modal statistical methods include mode estimation, regression, and clustering.
Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm
Modal linear regression (MLR) is a method for obtaining a conditional mode predictor as a linear model.
