Ichino, M.[Manabu],
Sklansky, J.[Jack],
The relative neighborhood graph for mixed feature variables,
PR(18), No. 2, 1985, pp. 161-167.
Elsevier DOI
0309
BibRef
Fränti, P.[Pasi],
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Iterative shrinking method for clustering problems,
PR(39), No. 5, May 2006, pp. 761-775.
Elsevier DOI 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.
Elsevier DOI
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.
Elsevier DOI
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
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 .