Lin, T.[Tong],
Zha, H.B.[Hong-Bin],
Riemannian Manifold Learning,
PAMI(30), No. 5, May 2008, pp. 796-809.
IEEE DOI
0803
BibRef
Lin, T.[Tong],
Zha, H.B.[Hong-Bin],
Lee, S.U.[Sang Uk],
Riemannian Manifold Learning for Nonlinear Dimensionality Reduction,
ECCV06(I: 44-55).
Springer DOI
0608
BibRef
Subbarao, R.[Raghav],
Meer, P.[Peter],
Nonlinear Mean Shift over Riemannian Manifolds,
IJCV(84), No. 1, August 2009, pp. xx-yy.
Springer DOI
0905
BibRef
Earlier:
Discontinuity Preserving Filtering over Analytic Manifolds,
CVPR07(1-6).
IEEE DOI
0706
BibRef
Earlier:
Nonlinear Mean Shift for Clustering over Analytic Manifolds,
CVPR06(I: 1168-1175).
IEEE DOI
0606
BibRef
And:
Subspace Estimation Using Projection Based M-Estimators over Grassmann
Manifolds,
ECCV06(I: 301-312).
Springer DOI
0608
BibRef
Earlier:
Heteroscedastic Projection Based M-Estimators,
EEMCV05(III: 38-38).
IEEE DOI
0507
projection based estimator to eliminate RANSAC problems.
BibRef
Subbarao, R.[Raghav],
Genc, Y.[Yakup],
Meer, P.[Peter],
Robust unambiguous parametrization of the essential manifold,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Earlier:
Nonlinear Mean Shift for Robust Pose Estimation,
WACV07(6-6).
IEEE DOI
0702
BibRef
Zhao, K.[Kun],
Alavi, A.[Azadeh],
Wiliem, A.[Arnold],
Lovell, B.C.[Brian C.],
Efficient clustering on Riemannian manifolds:
A kernelised random projection approach,
PR(51), No. 1, 2016, pp. 333-345.
Elsevier DOI
1601
Riemannian manifolds
BibRef
Liu, T.,
Shi, Z.,
Liu, Y.,
Joint Normalization and Dimensionality Reduction on Grassmannian:
A Generalized Perspective,
SPLetters(25), No. 6, June 2018, pp. 858-862.
IEEE DOI
1806
geometry, image recognition, learning (artificial intelligence),
optimisation, Grassmann manifold, Riemannian geometry,
image-set recognition
BibRef
Xie, X.F.[Xiao-Feng],
Yu, Z.L.[Zhu Liang],
Gu, Z.H.[Zheng-Hui],
Li, Y.Q.[Yuan-Qing],
Classification of symmetric positive definite matrices based on
bilinear isometric Riemannian embedding,
PR(87), 2019, pp. 94-105.
Elsevier DOI
1812
Covariance feature, Dimensionality reduction,
Isometric projection, Riemannian manifold, Pattern classification
BibRef
Hechmi, S.[Sabra],
Gallas, A.[Abir],
Zagrouba, E.[Ezzeddine],
Multi-kernel sparse subspace clustering on the Riemannian manifold of
symmetric positive definite matrices,
PRL(125), 2019, pp. 21-27.
Elsevier DOI
1909
Sparse subspace clustering (SSC), Riemannian kernel,
Multi-kernel SSC, Face clustering
BibRef
Gao, Z.[Zhi],
Wu, Y.W.[Yu-Wei],
Bu, X.Y.[Xing-Yuan],
Yu, T.[Tan],
Yuan, J.S.[Jun-Song],
Jia, Y.D.[Yun-De],
Learning a robust representation via a deep network on symmetric
positive definite manifolds,
PR(92), 2019, pp. 1-12.
Elsevier DOI
1905
Feature aggregation, SPD Matrix, Riemannian manifold,
Deep convolutional network
BibRef
Yang, Z.,
Cheng, Y.,
Wu, H.,
Wang, H.,
Enhanced Matrix CFAR Detection With Dimensionality Reduction of
Riemannian Manifold,
SPLetters(27), 2020, pp. 2084-2088.
IEEE DOI
2012
Matrix CFAR detection, Riemannian manifold,
dimensionality reduction, Grassmann manifold,
orthonormal constraint optimization
BibRef
Chen, S.,
Harandi, M.,
Jin, X.,
Yang, X.,
Domain Adaptation by Joint Distribution Invariant Projections,
IP(29), 2020, pp. 8264-8277.
IEEE DOI
2008
Kernel, Covariance matrices, Training, Labeling, Estimation,
Optimization, Dimensionality reduction, L²-distance,
Riemannian optimization
BibRef
Chakraborty, R.[Rudrasis],
Bouza, J.[Jose],
Manton, J.H.[Jonathan H.],
Vemuri, B.C.[Baba C.],
ManifoldNet: A Deep Neural Network for Manifold-Valued Data With
Applications,
PAMI(44), No. 2, February 2022, pp. 799-810.
IEEE DOI
2201
Deep learning on geometric data, e.g. pose.Manifolds, Biomedical imaging,
Neural networks, Measurement, Standards, riemannian manifolds
BibRef
Chen, S.T.[Sen-Tao],
Zheng, L.[Lin],
Wu, H.[Hanrui],
Riemannian representation learning for multi-source domain adaptation,
PR(137), 2023, pp. 109271.
Elsevier DOI
2302
Convex optimization, Hellinger distance,
Multi-source domain adaptation, Representation learning, Riemannian manifold
BibRef
Yin, W.G.[Wan-Guang],
Ma, Z.M.[Zheng-Ming],
Liu, Q.Y.[Quan-Ying],
Discriminative subspace learning via optimization on Riemannian
manifold,
PR(139), 2023, pp. 109450.
Elsevier DOI
2304
Discriminative subspace learning,
Riemannian manifold optimization, Dimensionality reduction, Classification
BibRef
Ben Amor, B.[Boulbaba],
Arguillère, S.[Sylvain],
Shao, L.[Ling],
ResNet-LDDMM:
Advancing the LDDMM Framework Using Deep Residual Networks,
PAMI(45), No. 3, March 2023, pp. 3707-3720.
IEEE DOI
2302
Shape, Strain, Measurement, Kernel, Residual neural networks,
Point cloud compression, Computational anatomy,
Riemannian geometry
BibRef
Tayanov, V.[Vitaliy],
Krzyzak, A.[Adam],
Suen, C.Y.[Ching Y.],
Comparison of Stacking-based Classifier Ensembles using Euclidean and
Riemannian Geometries,
ICPR21(10359-10366)
IEEE DOI
2105
Geometry, Radio frequency, Manifolds, Stacking, Neural networks,
Vegetation, Prediction algorithms
BibRef
Schwarz, J.[Jonathan],
Draxler, F.[Felix],
Köthe, U.[Ullrich],
Schnörr, C.[Christoph],
Riemannian SOS-Polynomial Normalizing Flows,
GCPR20(218-231).
Springer DOI
2110
BibRef
Shao, H.[Hang],
Kumar, A.[Abhishek],
Fletcher, P.T.[P. Thomas],
The Riemannian Geometry of Deep Generative Models,
Diff-CVML18(428-4288)
IEEE DOI
1812
Manifolds, Jacobian matrices, Computational modeling, Measurement,
Geometry, Generators, Data models
BibRef
Li, Y.,
Lu, R.,
Riemannian Metric Learning based on Curvature Flow,
ICPR18(806-811)
IEEE DOI
1812
Manifolds, Geometry, Euclidean distance, Matrix decomposition,
Machine learning algorithms, Dimensionality reduction,
Riemannian curvature
BibRef
Tayanov, V.,
Krzyzak, A.,
Suen, C.Y.,
Prediction-based classification using learning on Riemannian
manifolds,
ICPR18(591-596)
IEEE DOI
1812
Manifolds, Measurement, Symmetric matrices, Radio frequency,
Tensile stress, Decision trees, Prediction algorithms
BibRef
Chen, K.,
Wu, X.,
Wang, R.,
Kittler, J.V.,
Riemannian kernel based Nyström method for approximate
infinite-dimensional covariance descriptors with application to image
set classification,
ICPR18(651-656)
IEEE DOI
1812
Manifolds, Kernel, Covariance matrices, Measurement,
Feature extraction, Symmetric matrices, Task analysis,
Reproducing Kernel Hilbert Space
BibRef
Ilea, I.[Ioana],
Bombrun, L.B.[Lionel Bombrun],
Said, S.[Salem],
Berthoumieu, Y.[Yannick],
Covariance Matrices Encoding Based on the Log-Euclidean and Affine
Invariant Riemannian Metrics,
Diff-CVML18(506-50609)
IEEE DOI
1812
Covariance matrices, Measurement, Manganese, Encoding,
Computational modeling, Feature extraction, Histograms
BibRef
Lohit, S.[Suhas],
Turaga, P.K.[Pavan K.],
Learning Invariant Riemannian Geometric Representations Using Deep
Nets,
Manifold17(1329-1338)
IEEE DOI
1802
Train deep neural nets whose final outputs are elements on a
Riemannian manifold.
Face, Geometry, Lighting, Machine learning, Manifolds, Neural networks
BibRef
Zheng, L.G.[Li-Gang],
Qiu, G.P.[Guo-Ping],
Huang, J.W.[Ji-Wu],
Clustering Symmetric Positive Definite Matrices on the Riemannian
Manifolds,
ACCV16(I: 400-415).
Springer DOI
1704
BibRef
Masci, J.,
Boscaini, D.,
Bronstein, M.M.,
Vandergheynst, P.,
Geodesic Convolutional Neural Networks on Riemannian Manifolds,
3DRR15(832-840)
IEEE DOI
1602
Eigenvalues and eigenfunctions
BibRef
Kim, K.I.[Kwang In],
Tompkin, J.[James],
Theobalt, C.[Christian],
Curvature-Aware Regularization on Riemannian Submanifolds,
ICCV13(881-888)
IEEE DOI
1403
Semi-supervised learning; manifold; regularization
BibRef
Yu, D.J.[Dong-Jun],
Hancock, E.R.[Edwin R.],
Smith, W.A.P.[William A. P.],
A Riemannian Self-Organizing Map,
CIAP09(229-238).
Springer DOI
0909
Generalize SOM to Riemannian space.
BibRef
Goh, A.[Alvina],
Vidal, R.[Rene],
Clustering and dimensionality reduction on Riemannian manifolds,
CVPR08(1-7).
IEEE DOI
0806
BibRef
Zhao, D.L.[De-Li],
Lin, Z.C.[Zhou-Chen],
Tang, X.[Xiaoou],
Classification via semi-Riemannian spaces,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Earlier:
Contextual Distance for Data Perception,
ICCV07(1-8).
IEEE DOI
0710
Context from nearest neighbors.
BibRef
Brun, A.[Anders],
Westin, C.F.[Carl-Fredrik],
Herberthson, M.[Magnus],
Knutsson, H.[Hans],
Fast Manifold Learning Based on Riemannian Normal Coordinates,
SCIA05(920-929).
Springer DOI
0506
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Spectral Clustering, Data Dimensionality Reduction .