Bi-Level Multi-View fuzzy Clustering with Exponential Distance

Kristina P. Sinaga
3/29/2025
cs.CVcs.LGmath.PR

No official code found. Best implementation: KristinaP09/EB-MVFCM (0 stars, last commit 2025-08-29).

Abstract

In this study, we propose extension of fuzzy c-means (FCM) clustering in multi-view environments. First, we introduce an exponential multi-view FCM (E-MVFCM). E-MVFCM is a centralized MVC with consideration to heat-kernel coefficients (H-KC) and weight factors. Secondly, we propose an exponential bi-level multi-view fuzzy c-means clustering (EB-MVFCM). Different to E-MVFCM, EB-MVFCM does automatic computation of feature and weight factors simultaneously. Like E-MVFCM, EB-MVFCM present explicit forms of the H-KC to simplify the generation of the heat-kernel $\mathcal{K}(t)$ in powers of the proper time $t$ during the clustering process. All the features used in this study, including tools and functions of proposed algorithms will be made available at https://www.github.com/KristinaP09/EB-MVFCM.

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Code Implementations(1)

KristinaP09/EB-MVFCMCommunity100%

Multi-view data and Matlab-code for Heat-Kernel Multi-View Learning

00Apr 11, 20251 years ago
multi-viewmultimodalunsupervised-machine-learning

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Cite this paper

@article{sinaga2025bilevel,
  title  = {Bi-Level Multi-View fuzzy Clustering with Exponential Distance},
  author = {Kristina P. Sinaga},
  year   = {2025},
  eprint = {2503.22932},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.22932v1}
}

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