Hà Quang Minh
According to our database^{1},
Hà Quang Minh
authored at least 26 papers
between 2004 and 2020.
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Bibliography
2020
EntropyRegularized 2Wasserstein Distance between Gaussian Measures.
CoRR, 2020
2019
A Unified Formulation for the BuresWasserstein and LogEuclidean/LogHilbertSchmidt Distances between Positive Definite Operators.
Proceedings of the Geometric Science of Information  4th International Conference, 2019
2017
Covariances in Computer Vision and Machine Learning
Synthesis Lectures on Computer Vision, Morgan & Claypool Publishers, 2017
LogDeterminant Divergences Between Positive Definite HilbertSchmidt Operators.
Proceedings of the Geometric Science of Information  Third International Conference, 2017
2016
A Unifying Framework in Vectorvalued Reproducing Kernel Hilbert Spaces for Manifold Regularization and CoRegularized Multiview Learning.
J. Mach. Learn. Res., 2016
Infinitedimensional LogDeterminant divergences II: AlphaBeta divergences.
CoRR, 2016
Kernel Methods on Approximate InfiniteDimensional Covariance Operators for Image Classification.
CoRR, 2016
OperatorValued Bochner Theorem, Fourier Feature Maps for OperatorValued Kernels, and VectorValued Learning.
CoRR, 2016
Approximate LogHilbertSchmidt Distances between Covariance Operators for Image Classification.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016
2015
Kernelbased classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
AffineInvariant Riemannian Distance Between InfiniteDimensional Covariance Operators.
Proceedings of the Geometric Science of Information  Second International Conference, 2015
2014
LogHilbertSchmidt metric between positive definite operators on Hilbert spaces.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014
2013
Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation.
IEEE Trans. Image Process., 2013
Scalable Matrixvalued Kernel Learning for Highdimensional Nonlinear Multivariate Regression and Granger Causality.
Proceedings of the TwentyNinth Conference on Uncertainty in Artificial Intelligence, 2013
A unifying framework for vectorvalued manifold regularization and multiview learning.
Proceedings of the 30th International Conference on Machine Learning, 2013
Trusting Skype: Learning the Way People Chat for Fast User Recognition and Verification.
Proceedings of the 2013 IEEE International Conference on Computer Vision Workshops, 2013
Semisupervised multifeature learning for person reidentification.
Proceedings of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013
2012
Scalable Matrixvalued Kernel Learning and Highdimensional Nonlinear Causal Inference
CoRR, 2012
A regularized spectral algorithm for Hidden Markov Models with applications in computer vision.
Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012
2011
A New KernelBased Approach for NonlinearSystem Identification.
IEEE Trans. Autom. Control., 2011
The regularized least squares algorithm and the problem of learning halfspaces.
Inf. Process. Lett., 2011
Vectorvalued Manifold Regularization.
Proceedings of the 28th International Conference on Machine Learning, 2011
Slow feature analysis and decorrelation filtering for separating correlated sources.
Proceedings of the IEEE International Conference on Computer Vision, 2011
2010
Image and Video Colorization Using VectorValued Reproducing Kernel Hilbert Spaces.
J. Math. Imaging Vis., 2010
2006
Mercer's Theorem, Feature Maps, and Smoothing.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006
2004
Learning Over Compact Metric Spaces.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004