positive semi-definite kernel as a sample mean in the associated reproducing kernel ... to find a robust sample mean of the Φ(Xi)'s. For ... Definition 7 (IF for ...

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May 1, 2014 · We propose a robustification of the mean-shift algorithm. We understand robustness in the statistical sense as the deviation from the ...

A robust kernel density estimator based mean-shift algorithm · Nevine DemitriA ... A robust nonparametric density estimator combining the popular Kernel Density ...

We prove for discrete data the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its ...

Compared with QMDPE, MKDE does not require running the mean shift algorithm for each candidate fit. Thus, the computational complexity of MKDE is greatly ...

Jul 15, 2011 · We interpret the KDE based on a radial, positive semi-definite kernel as a sample mean in the associated reproducing kernel Hilbert space. Since ...

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Jan 8, 2024 · ... based methods like k-means that optimize for low variance around a mean. Mean shift is less sensitive as it shifts based on local density.

### Mean shift-based clustering - ScienceDirect.com

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In this paper, a mean shift-based clustering algorithm is proposed. The mean shift is a kernel-type weighted mean procedure. Herein, we first discuss three ...

Jun 14, 2013 · So basically all the points are considered in calculation of the mean shift but there is a weight assigned to each point that decays ...

Mean shift is a non-parametric feature-space mathematical analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm.