Open AccessSimilarity Graph Learning and Non-linear Deep Representations for Spectral Clusterings
Author(s) -
Ni Li,
Manman Peng,
Buwen Cao,
Kenli Li,
Keqin Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012001
Subject(s) - spectral clustering , cluster analysis , graph , laplacian matrix , artificial intelligence , pattern recognition (psychology) , spectral graph theory , computer science , dimensionality reduction , representation (politics) , feature learning , similarity (geometry) , correlation clustering , mathematics , theoretical computer science , voltage graph , line graph , politics , political science , law , image (mathematics)
Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )