An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity

An Improvement of Spectral Clustering via Message Passing and Density Sensitive Similarity

Spectral clustering transforms the data clustering problem into a graph-partitioning problem and classifies data points by finding the optimal sub-graphs. Traditional spectral clustering algorithms use Gaussian kernel function to construct the similarity matrix, so they are sensitive to the selectio...

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Bibliographic Details
Main Authors: Lijuan Wang, Shifei Ding, Hongjie Jia
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Spectral clustering
similarity matrix
message passing
clustering stability
Online Access:https://ieeexplore.ieee.org/document/8766800/

Internet

https://ieeexplore.ieee.org/document/8766800/

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