13.3.8.11 Graph Clustering, Cilque Generation

Chapter Contents (Back)
Graph Clustering.

Rizzi, S.[Stefano],
Genetic operators for hierarchical graph clustering,
PRL(19), No. 14, December 1998, pp. 1293-1300. BibRef 9812

Bunke, H.,
Inexact Graph Matching for Structural Pattern Recognition,
PRL(1), No. 4, 1983, pp. 245-253. BibRef 8300

Günter, S.[Simon], Bunke, H.[Horst],
Self-organizing map for clustering in the graph domain,
PRL(23), No. 4, February 2002, pp. 405-417.
Elsevier DOI 0202
BibRef

Günter, S.[Simon], Bunke, H.[Horst],
Validation indices for graph clustering,
PRL(24), No. 8, May 2003, pp. 1107-1113.
Elsevier DOI 0304
BibRef

Zanghi, H.[Hugo], Ambroise, C.[Christophe], Miele, V.[Vincent],
Fast online graph clustering via Erdos-Renyi mixture,
PR(41), No. 12, December 2008, pp. 3592-3599.
Elsevier DOI 0810
EM algorithm; Graph clustering; Online BibRef

Zanghi, H.[Hugo], Volant, S.[Stevenn], Ambroise, C.[Christophe],
Clustering based on random graph model embedding vertex features,
PRL(31), No. 9, 1 July 2010, pp. 830-836.
Elsevier DOI 1004
Variational EM algoritm; Graph clustering; Vertex features BibRef

Rota Bulň, S.[Samuel], Pelillo, M.[Marcello],
A Game-Theoretic Approach to Hypergraph Clustering,
PAMI(35), No. 6, June 2013, pp. 1312-1327.
IEEE DOI 1305
Extract coherent groups using high-order (not pairwise) similarities. BibRef

Kontschieder, P.[Peter], Rota Bulo, S.[Samuel], Pelillo, M.[Marcello], Bischof, H.[Horst],
Structured Labels in Random Forests for Semantic Labelling and Object Detection,
PAMI(36), No. 10, October 2014, pp. 2104-2116.
IEEE DOI 1410
BibRef
Earlier: A2, A1, A3, A4:
Structured Local Predictors for image labelling,
CVPR12(3530-3537).
IEEE DOI 1208
BibRef
Earlier: A1, A2, A4, A3:
Structured class-labels in random forests for semantic image labelling,
ICCV11(2190-2197).
IEEE DOI 1201
Structural information in Random Forest framework. BibRef

Wu, J., Pan, S., Zhu, X., Cai, Z.,
Boosting for Multi-Graph Classification,
Cyber(45), No. 3, March 2015, pp. 430-443.
IEEE DOI 1502
Algorithm design and analysis BibRef

Pan, S., Wu, J., Zhu, X., Zhang, C.,
Graph Ensemble Boosting for Imbalanced Noisy Graph Stream Classification,
Cyber(45), No. 5, May 2015, pp. 940-954.
IEEE DOI 1505
Accuracy BibRef

Tahaei, M.S.[Maedeh S.], Hashemi, S.N.[Seyed Naser],
Graph Characterization by Counting Sink Star Subgraphs,
JMIV(57), No. 3, March 2017, pp. 439-454.
Springer DOI 1702
BibRef

Pelillo, M.[Marcello], Elezi, I.[Ismail], Fiorucci, M.[Marco],
Revealing structure in large graphs: Szemerédi's regularity lemma and its use in pattern recognition,
PRL(87), No. 1, 2017, pp. 4-11.
Elsevier DOI 1703
Graph-theoretic methods BibRef

Meng, Z.[Zhaoyi], Merkurjev, E.[Ekaterina], Koniges, A.[Alice], Bertozzi, A.L.[Andrea L.],
Hyperspectral Image Classification Using Graph Clustering Methods,
IPOL(7), 2017, pp. 218-245.
DOI Link 1708
Code, Hyperspectral Classification. Initial description: See also Multi-class Graph Mumford-Shah Model for Plume Detection Using the MBO scheme. See also Graph MBO method for multiclass segmentation of hyperspectral stand-off detection video. Parallel Implementation: See also OpenMP parallelization and optimization of graph-based machine learning algorithms. BibRef

Qin, Y.K.[Yi-Kun], Yu, Z.L.[Zhu Liang], Wang, C.D.[Chang-Dong], Gu, Z.H.[Zheng-Hui], Li, Y.Q.[Yuan-Qing],
A Novel clustering method based on hybrid K-nearest-neighbor graph,
PR(74), No. 1, 2018, pp. 1-14.
Elsevier DOI 1711
Graph clustering BibRef

Zhan, K., Nie, F., Wang, J., Yang, Y.,
Multiview Consensus Graph Clustering,
IP(28), No. 3, March 2019, pp. 1261-1270.
IEEE DOI 1812
graph theory, iterative methods, matrix algebra, optimisation, pattern clustering, unsupervised learning, optimization problem, graph learning BibRef

Chen, M.[Mulin], Wang, Q.[Qi], Li, X.L.[Xue-Long],
Discriminant Analysis with Graph Learning for Hyperspectral Image Classification,
RS(10), No. 6, 2018, pp. xx-yy.
DOI Link 1806
BibRef
And:
Robust Adaptive Sparse Learning Method for Graph Clustering,
ICIP18(1618-1622)
IEEE DOI 1809
Robustness, Manifolds, Linear programming, Clustering algorithms, Sparse matrices, Optimization, Toy manufacturing industry, Sparse Learning BibRef

Huang, S.D.[Shu-Dong], Kang, Z.[Zhao], Tsang, I.W.[Ivor W.], Xu, Z.L.[Zeng-Lin],
Auto-weighted multi-view clustering via kernelized graph learning,
PR(88), 2019, pp. 174-184.
Elsevier DOI 1901
Graph learning, Multi-view clustering, Multiple kernel learning, Auto-weighted strategy BibRef

Huang, S.D.[Shu-Dong], Kang, Z.[Zhao], Xu, Z.L.[Zeng-Lin],
Auto-weighted multi-view clustering via deep matrix decomposition,
PR(97), 2020, pp. 107015.
Elsevier DOI 1910
Multi-view learning, Deep matrix decomposition, Clustering, Optimization algorithm BibRef

Lu, X.M.[Xiao-Min], Yan, H.[Haowen], Li, W.[Wende], Li, X.J.[Xiao-Jun], Wu, F.[Fang],
An Algorithm based on the Weighted Network Voronoi Diagram for Point Cluster Simplification,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903
Clustering using the roads that connect the points (towns). BibRef

Araghi, H., Sabbaqi, M., Babaie-Zadeh, M.,
K-Graphs: An Algorithm for Graph Signal Clustering and Multiple Graph Learning,
SPLetters(26), No. 10, October 2019, pp. 1486-1490.
IEEE DOI 1909
Clustering algorithms, Signal processing algorithms, Laplace equations, Symmetric matrices, Estimation, graph Laplacian matrix BibRef

Kim, Y.[Younghoon], Do, H.[Hyungrok], Kim, S.B.[Seoung Bum],
Outer-Points shaver: Robust graph-based clustering via node cutting,
PR(97), 2020, pp. 107001.
Elsevier DOI 1910
Graph-based clustering, Unsupervised learning, Spectral clustering, Pseudo-density reconstruction, Node cutting BibRef

Wang, R., Nie, F., Wang, Z., He, F., Li, X.,
Scalable Graph-Based Clustering With Nonnegative Relaxation for Large Hyperspectral Image,
GeoRS(57), No. 10, October 2019, pp. 7352-7364.
IEEE DOI 1910
computational complexity, eigenvalues and eigenfunctions, geophysical image processing, graph theory, nonnegative relaxation BibRef

Fan, X.L.[Xiao-Long], Gong, M.[Maoguo], Xie, Y.[Yu], Jiang, F.L.[Fen-Long], Li, H.[Hao],
Structured self-attention architecture for graph-level representation learning,
PR(100), 2020, pp. 107084.
Elsevier DOI 2005
Neural self-attention mechanism, Graph neural networks, Graph classification BibRef

Wu, T.[Tong],
Graph regularized low-rank representation for submodule clustering,
PR(100), 2020, pp. 107145.
Elsevier DOI 2005
Clustering, Kernel methods, Manifold regularization, Submodule clustering, Tensor nuclear norm, Union of free submodules BibRef


Geng, Z.Q.[Zhi-Qiang], Li, Z.K.[Zhong-Kun], Han, Y.M.[Yong-Ming],
A Novel Asymmetric Embedding Model for Knowledge Graph Completion,
ICPR18(290-295)
IEEE DOI 1812
Space vehicles, Orbits, Training, Complexity theory, Knowledge engineering, Benchmark testing, Predictive models, Asymmetrical Embedding BibRef

Niu, J.H.[Jing-Hao], Sun, Z.Y.[Zheng-Ya], Zhang, W.S.[Wen-Sheng],
Enhancing Knowledge Graph Completion with Positive Unlabeled Learning,
ICPR18(296-301)
IEEE DOI 1812
Reliability, Logistics, Predictive models, Correlation, Semantics, Data models, Training BibRef

Ikami, D., Yamasaki, T., Aizawa, K.,
Local and Global Optimization Techniques in Graph-Based Clustering,
CVPR18(3456-3464)
IEEE DOI 1812
Cost function, Optimization methods, Linear programming, Sparse matrices, Computer vision, Clustering algorithms BibRef

Flores-Garrido, M.[Marisol], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José F.],
Graph Clustering via Inexact Patterns,
CIARP14(391-398).
Springer DOI 1411
BibRef

García-Borroto, M.[Milton], Villuendas-Rey, Y.[Yenny], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José F.],
Finding Small Consistent Subset for the Nearest Neighbor Classifier Based on Support Graphs,
CIARP09(465-472).
Springer DOI 0911
BibRef

García-Borroto, M.[Milton], Villuendas-Rey, Y.[Yenny], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José F.],
Using Maximum Similarity Graphs to Edit Nearest Neighbor Classifiers,
CIARP09(489-496).
Springer DOI 0911
BibRef

Suárez, A.P.[Airel Pérez], Trinidad, J.F.M.[José F. Martínez], Carrasco Ochoa, J.A.[Jesús A.], Medina Pagola, J.E.[José E.],
A New Incremental Algorithm for Overlapped Clustering,
CIARP09(497-504).
Springer DOI 0911
BibRef

Tan, M.[Mingkui], Shi, Q.[Qinfeng], van den Hengel, A.J.[Anton J.], Shen, C.H.[Chun-Hua], Gao, J.B.[Jun-Bin], Hu, F.Y.[Fu-Yuan], Zhang, Z.[Zhen],
Learning Graph Structure for Multi-Label Image Classification Via Clique Generation,
CVPR15(4100-4109)
IEEE DOI 1510
BibRef

Donoser, M.[Michael],
Replicator Graph Clustering,
BMVC13(xx-yy).
DOI Link 1402
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Graph Embedding Clustering .


Last update:Jun 29, 2020 at 10:24:28