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A recent paper (Neural Networks, {\bf 132} (2020), 253-268) introduces a straightforward and simple kernel based approximation for manifold learning that does not require the knowledge of anything about the manifold, except for its dimension.
Orthogonal polynomials
G. Szegö · 1975
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A new criterion for automatic multilevel thresholding
J.-C. Yen, F.-J. Chang, and S. Chang · 1995
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Orthogonal polynomials: computation and approximation
W. Gautschi · 2004
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Special issue: Diffusion maps and wavelets
C. K. Chui and D. L. Donoho · 2006
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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A quadrature formula for diffusion polynomials corresponding to a generalized heat kernel
F. Filbir and H. N. Mhaskar · 2010
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Eignets for function approximation on manifolds
H. N. Mhaskar · 2010
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Marcinkiewicz–Zygmund measures on manifolds
F. Filbir and H. N. Mhaskar · 2011
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Signal Ensemble Classification Using Low-Dimensional Embeddings and Earth Mover’s Distance
L. Lieu and N. Saito · 2011
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Scikit-learn: Machine learning in python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al · 2011
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Statistical computations on grassmann and stiefel manifolds for image and video-based recognition
P. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa · 2011
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Classification of small uavs and birds by micro-doppler signatures
P. Molchanov, R. I. Harmanny, J. J. de Wit, K. Egiazarian, and J. Astola · 2014
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Features for micro-doppler based activity classification
S. Björklund, H. Petersson, and G. Hendeby · 2015
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Operational assessment and adaptive selection of micro-doppler features
S. Z. Gürbüz, B. Erol, B. Çağlıyan, and B. Tekeli · 2015
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Knowledge exploitation for human micro-doppler classification
C. Karabacak, S. Z. Gurbuz, A. C. Gurbuz, M. B. Guldogan, G. Hendeby, and F. Gustafsson · 2015
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Review of micro-doppler signatures
D. Tahmoush · 2015
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Hand gesture recognition using micro-doppler signatures with convolutional neural network
Y. Kim and B. Toomajian · 2016
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Micro-doppler based classification of human aquatic activities via transfer learning of convolutional neural networks
J. Park, R. J. Javier, T. Moon, and Y. Kim · 2016
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Schubert varieties and distances between subspaces of different dimensions
K. Ye and L.-H. Lim · 2016
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Measuring the tendency of cnns to learn surface statistical regularities
J. Jo and Y. Bengio · 2017
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An adaptive s-method to analyze micro-doppler signals for human activity classification
F. Li, C. Yang, Y. Xia, X. Ma, T. Zhang, and Z. Zhou · 2017
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Theory of deep learning III: explaining the non-overfitting puzzle
T. Poggio, K. Kawaguchi, Q. Liao, B. Miranda, L. Rosasco, X. Boix, J. Hidary, and H. Mhaskar · 2017
Cited alongside, same era.
Micro-Doppler Gesture Recognition using Doppler, Time and Range Based Features
M. Ritchie and A. M. Jones · 2019
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Deep ReLU network approximation of functions on a manifold
J. Schmidt-Hieber · 2019
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Dnn transfer learning from diversified micro-doppler for motion classification
M. S. Seyfioglu, B. Erol, S. Z. Gurbuz, and M. G. Amin · 2019
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Prologue: Perspectives on deep learning of rf data
S. Z. Gurbuz and E. Mason · 2020
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A direct method for function approximation on data defined manifolds
H. N. Mhaskar · 2020
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Micro-doppler based human-robot classification using ensemble and deep learning approaches
S. Abdulatif, Q. Wei, F. Aziz, B. Kleiner, and U. Schneider · 2018
Cited alongside, same era.
Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate
M. Belkin, D. J. Hsu, and P. Mitra · 2018
Cited alongside, same era.
Temporal deep learning for drone micro-doppler classification
D. A. Brooks, O. Schwander, F. Barbaresco, J.-Y. Schneider, and M. Cord · 2018
Cited alongside, same era.
Deep nets for local manifold learning
C. K. Chui and H. N. Mhaskar · 2018
Cited alongside, same era.
Data-driven cepstral and neural learning of features for robust micro-Doppler classification
B. Erol, M. S. Seyfioglu, S. Z. Gurbuz, and M. Amin · 2018
Cited alongside, same era.
Sparsity-driven micro-doppler feature extraction for dynamic hand gesture recognition
G. Li, R. Zhang, M. Ritchie, and H. Griffiths · 2018
Cited alongside, same era.
Theory IIIb: Generalization in deep networks
T. Poggio, Q. Liao, B. Miranda, A. Banburski, X. Boix, and J. Hidary · 2018
Cited alongside, same era.
H. N. Mhaskar · 2020
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An analysis of training and generalization errors in shallow and deep networks
H. N. Mhaskar and T. Poggio · 2020
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Dop-net: a micro-doppler radar data challenge
M. Ritchie, R. Capraru, and F. Fioranelli · 2020
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Deep-learning methods for hand-gesture recognition using ultra-wideband radar
S. Skaria, A. Al-Hourani, and R. J. Evans · 2020
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Transfer learning from audio deep learning models for micro-doppler activity recognition
K. T. Tran, L. D. Griffin, K. Chetty, and S. Vishwakarma · 2020
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Automatic arm motion recognition based on radar micro-doppler signature envelopes
Z. Zeng, M. G. Amin, and T. Shan · 2020
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Mmw radar-based technologies in autonomous driving: A review
T. Zhou, M. Yang, K. Jiang, H. Wong, and D. Yang · 2020
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A hybrid cnn–lstm network for the classification of human activities based on micro-doppler radar
J. Zhu, H. Chen, and W. Ye · 2020
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Hand gestures recognition using radar sensors for human-computer-interaction: A review
S. Ahmed, K. D. Kallu, S. Ahmed, and S. H. Cho · 2021
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Measurements and analysis of the doppler signature of a human moving within the forest in uhf-band
G. Manfredi, I. D. S. Hinostroza, M. Menelle, S. Saillant, J.-P. Ovarlez, and L. Thirion-Lefevre · 2021
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Uav recognition based on micro-doppler dynamic attribute-guided augmentation algorithm
C. Zhao, G. Luo, Y. Wang, C. Chen, and Z. Wu · 2021
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