Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection

Self-Expressive Kernel Subspace Clustering Algorithm for Categorical Data with Embedded Feature Selection

Kernel clustering of categorical data is a useful tool to process the separable datasets and has been employed in many disciplines. Despite recent efforts, existing methods for kernel clustering remain a significant challenge due to the assumption of feature independence and equal weights. In this s...

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Bibliographic Details
Main Authors: Hui Chen, Kunpeng Xu, Lifei Chen, Qingshan Jiang
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Mathematics
Subjects:
machine learning
categorical data
similarity
feature selection
kernel density estimation
non-linear optimization
Online Access:https://www.mdpi.com/2227-7390/9/14/1680

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