Seth, S.[Sohan],
Eugster, M.J.A.[Manuel J. A.],
Archetypal Analysis for Nominal Observations,
PAMI(38), No. 5, May 2016, pp. 849-861.
IEEE DOI
1604
explains a set of observations as compositions of few pure patterns.
Bayes methods
BibRef
Ni, D.,
Ma, H.,
Fast Classification of Hyperspectral Images Using Globally
Regularized Archetypal Representation With Approximate Solution,
GeoRS(55), No. 4, April 2017, pp. 2414-2430.
IEEE DOI
1704
approximation theory
BibRef
Sun, W.W.[Wei-Wei],
Zhang, D.[Dianfa],
Xu, Y.[Yan],
Tian, L.[Long],
Yang, G.[Gang],
Li, W.[Weiyue],
A Probabilistic Weighted Archetypal Analysis Method with Earth
Mover's Distance for Endmember Extraction from Hyperspectral Imagery,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link
1708
BibRef
Sun, W.W.[Wei-Wei],
Yang, G.[Gang],
Wu, K.[Ke],
Li, W.Y.[Wei-Yue],
Zhang, D.[Dianfa],
Pure endmember extraction using robust kernel archetypoid analysis
for hyperspectral imagery,
PandRS(131), No. 1, 2017, pp. 147-159.
Elsevier DOI
1709
Endmember, extraction
BibRef
Keller, S.M.[Sebastian Mathias],
Samarin, M.[Maxim],
Torres, F.A.[Fabricio Arend],
Wieser, M.[Mario],
Roth, V.[Volker],
Learning Extremal Representations with Deep Archetypal Analysis,
IJCV(129), No. 4, April 2021, pp. 805-820.
Springer DOI
2104
BibRef
Earlier: A1, A2, A4, A5, Only:
Deep Archetypal Analysis,
GCPR19(171-185).
Springer DOI
1911
Award, GCPR, HM. Representations of high-dimensional datasets in terms of intuitively
understandable basic entities called archetypes.
BibRef
Fotiadou, E.[Eftychia],
Panagakis, Y.F.[Yi-Fannis],
Pantic, M.[Maja],
Temporal Archetypal Analysis for Action Segmentation,
FG17(490-496)
IEEE DOI
1707
Convergence, Data mining, Feature extraction, Optimization,
Symmetric matrices, Time series analysis, Visualization
BibRef
Bauckhage, C.[Christian],
Manshaei, K.[Kasra],
Kernel Archetypal Analysis for Clustering Web Search Frequency Time
Series,
ICPR14(1544-1549)
IEEE DOI
1412
Data models
BibRef
Chen, Y.[Yuansi],
Mairal, J.[Julien],
Harchaoui, Z.[Zaid],
Fast and Robust Archetypal Analysis for Representation Learning,
CVPR14(1478-1485)
IEEE DOI
1409
archetypal analysis; sparse coding
BibRef
Kaufmann, D.[Dinu],
Keller, S.[Sebastian],
Roth, V.[Volker],
Copula Archetypal Analysis,
GCPR15(117-128).
Springer DOI
1511
BibRef
Prabhakaran, S.[Sandhya],
Raman, S.[Sudhir],
Vogt, J.E.[Julia E.],
Roth, V.[Volker],
Automatic Model Selection in Archetype Analysis,
DAGM12(458-467).
Springer DOI
1209
Representative objects (text)
BibRef
Bauckhage, C.[Christian],
Thurau, C.[Christian],
Adapting Information Theoretic Clustering to Binary Images,
ICPR10(910-913).
IEEE DOI
1008
BibRef
Earlier:
Making Archetypal Analysis Practical,
DAGM09(272-281).
Springer DOI
0909
Represent as combination of extremal points.
BibRef
Thurau, C.[Christian],
Nearest Archetype Hull Methods for Large-Scale Data Classification,
ICPR10(4040-4043).
IEEE DOI
1008
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
K-Means Clustering .