This paper proposes a novel multiple kernel learning frame- work for clustering and semi-supervised classification. In this model, a more flexible kernel ...
The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on ...
This paper proposes a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus kernel; and (ii) the ...
The proposed framework is integrated into a unified framework for graph-based clustering and semi-supervised classification. We have conducted experiments on ...
Jul 13, 2018 · In this paper, we propose a novel MKL framework by following two intuitive assumptions: (i) each kernel is a perturbation of the consensus ...
Jun 20, 2018 · Self-weighted Multiple Kernel Learning for Graph-based. Clustering and Semi-supervised Classification. Zhao Kang1∗, Xiao Lu1, Jinfeng Yi2 ...
Jul 16, 2023 · Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification. Zhao Kang, Xiao Lu, Jinfeng Yi, Zenglin ...
Oct 25, 2023 · Self-weighted Multiple Kernel Learning for Graph-based Clustering and Semi-supervised Classification(arXiv). Author : Zhao Kang, Xiao Lu ...
Nie et al. Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. Proceedings of the ...
Feb 15, 2024 · This framework integrates these initial similar graph matrices into a unified graph matrix by assigning different weights to the initial graphs ...