Local and Global Structure Preservation Based Spectral Clustering

2022-10-23 · Kajal Eybpoosh, Mansoor Rezghi, Abbas Heydari

Spectral Clustering (SC) is widely used for clustering data on a nonlinear manifold. SC aims to cluster data by considering the preservation of the local neighborhood structure on the manifold data. This paper extends Spectral Clustering to Local and Global Structure Preservation Based Spectral Clustering (LGPSC) that incorporates both global structure and local neighborhood structure simultaneously. For this extension, LGPSC proposes two models to extend local structures preservation to local and global structures preservation: Spectral clustering guided Principal component analysis model and Multilevel model. Finally, we compare the experimental results of the state-of-the-art methods with our two models of LGPSC on various data sets such that the experimental results confirm the effectiveness of our LGPSC models to cluster nonlinear data.

📄 PDF Abstract BibTeX arXiv:2210.12778

Code (0)

등록된 구현이 없습니다.

Tasks

Clustering

Methods 이 논문이 사용한 방법론

Spectral Clustering Spectral clustering has attracted increasing attention due to the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the…

Efficient Multi-View Graph Clustering with Local and Global Structure Preservation

2023-08-31 · Yi Wen, Suyuan Liu, Xinhang Wan, Siwei Wang 외

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing to its high efficiency and the capability to capture complementary structural information across multiple views. Intuitively, a high-…

Clustering Graph Clustering Graph Learning

A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction

2026-04-02 · Zeyang Huang, Angelos Chatzimparmpas, Thomas Höllt, Takanori Fujiwara arxiv

Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure. Neighbor embedding methods such as t-SNE and UMAP prioritize local simil…

Dimensionality Reduction

Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation

2020-09-21 · Lirong Wu, Zicheng Liu, Zelin Zang, Jun Xia 외

Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss may corrupt the local and global structu…

Clustering Deep Clustering Representation Learning

FedSpectral+: Spectral Clustering using Federated Learning

2023-02-04 · Janvi Thakkar, Devvrat Joshi

Clustering in graphs has been a well-known research problem, particularly because most Internet and social network data is in the form of graphs. Organizations widely use spectral clustering algorithms to find clustering…

Clustering Federated Learning

PASCO (PArallel Structured COarsening): an overlay to speed up graph clustering algorithms

2024-12-18 · Etienne Lasalle, Rémi Vaudaine, Titouan Vayer, Pierre Borgnat 외

Clustering the nodes of a graph is a cornerstone of graph analysis and has been extensively studied. However, some popular methods are not suitable for very large graphs: e.g., spectral clustering requires the computatio…

Clustering Computational Efficiency Graph Clustering