14.2.11.2 Neighborhood Graph Classification

Chapter Contents (Back)
Neighborhood Graph.

Ichino, M.[Manabu], Sklansky, J.[Jack],
The relative neighborhood graph for mixed feature variables,
PR(18), No. 2, 1985, pp. 161-167.
WWW Link. 0309
BibRef

Fränti, P.[Pasi], Virmajoki, O.[Olli],
Iterative shrinking method for clustering problems,
PR(39), No. 5, May 2006, pp. 761-775.
WWW Link. Vector quantization; Codebook generation; Agglomeration; PNN 0604
BibRef
Earlier: A2, A1:
Divide-and-conquer algorithm for creating neighborhood graph for clustering,
ICPR04(I: 264-267).
IEEE DOI 0409
BibRef

Virmajoki, O., Franti, P., Kaukoranta, T.,
Iterative shrinking method for generating clustering,
ICIP02(II: 685-688).
IEEE DOI 0210
BibRef

Wen, G.H.[Gui-Hua], Jiang, L.J.[Li-Jun], Wen, J.[Jun],
Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding,
PR(41), No. 7, July 2008, pp. 2226-2236.
WWW Link. 0804
BibRef
And: Authors' response: PR(42), No. 5, May 2009, pp. 1014.
Elsevier DOI 0902
Isometric data embedding; Nonlinear neighborhood; Neighborhood graph; Geodesic distance; Manifold learning BibRef

Wen, G.H.[Gui-Hua], Jiang, L.J.[Li-Jun], Wen, J.[Jun],
Local relative transformation with application to isometric embedding,
PRL(30), No. 3, 1 February 2009, pp. 203-211.
Elsevier DOI 0804
Isometric embedding; Cognitive law; Relative transformation; Local relative transformation; Neighborhood graph; Manifold learning BibRef

Zhong, C.M.[Cai-Ming], Miao, D.Q.[Duo-Qian],
A comment on 'Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding',
PR(42), No. 5, May 2009, pp. 1012-1013.
Elsevier DOI 0902
Triangle inequality; Geodesic distance; Euclidean distance See also Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding. BibRef

Meng, D.Y.[De-Yu], Leung, Y.[Yee], Xu, Z.B.[Zong-Ben], Fung, T.[Tung], Zhang, Q.F.[Qing-Fu],
Improving geodesic distance estimation based on locally linear assumption,
PRL(29), No. 7, 1 May 2008, pp. 862-870.
WWW Link. 0804
Isometric feature mapping; Geodesic distance estimation; Neighborhood graph; Nonlinear dimensionality reduction BibRef

Yang, Y.[Yi], Han, D.Q.[De-Qiang], Dezert, J.[Jean],
An angle-based neighborhood graph classifier with evidential reasoning,
PRL(71), No. 1, 2016, pp. 78-85.
Elsevier DOI 1602
Neighborhood classifier BibRef


Jothi, R., Mohanty, S.K.[Sraban Kumar], Ojha, A.[Aparajita],
Fast Minimum Spanning Tree Based Clustering Algorithms on Local Neighborhood Graph,
GbRPR15(292-301).
Springer DOI 1511
BibRef

Wang, J.[Jing], Wang, J.D.[Jing-Dong], Zeng, G.[Gang], Gan, R.[Rui], Li, S.P.[Shi-Peng], Guo, B.[Baining],
Fast Neighborhood Graph Search Using Cartesian Concatenation,
ICCV13(2128-2135)
IEEE DOI 1403
new data structure for approximate nearest neighbor search BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Optimal Path Forest Classification .


Last update:Sep 18, 2017 at 11:34:11