Kernel optimization strategy based on mean shift

Kernel optimization strategy based on mean shift

A kernel optimization strategy based on mean shift was proposed to improve the classification performance of samples. After doing mean shift optimization, the samples move to the center of local high density and the distances of samples in the same class are closer than the distances of the samples...

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Bibliographic Details
Published in:2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) pp. 1577 - 1582
Main Authors: Zhiqiang Wen, Zhigao Zeng, Wenqiu Zhu
Format: Conference Proceeding
Language:English
Published: IEEE 01-08-2015
Subjects:
Algorithm design and analysis
Bandwidth
classification
Error analysis
Kernel
kernel optimization
Manifolds
mean shift
Optimization
Training
width
Online Access:Get full text
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Summary:A kernel optimization strategy based on mean shift was proposed to improve the classification performance of samples. After doing mean shift optimization, the samples move to the center of local high density and the distances of samples in the same class are closer than the distances of the samples in different classes. We design an iterated local search strategy for automatic bandwidth selection of mean shift optimization. In experiments, we demonstrated the performance on various data sets. According to our experiment study, our method can improve the discrimination of samples.
DOI:10.1109/FSKD.2015.7382180