Hà Quang Minh
According to our database1, Hà Quang Minh authored at least 26 papers between 2004 and 2020.
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Entropy-Regularized 2-Wasserstein Distance between Gaussian Measures.
A Unified Formulation for the Bures-Wasserstein and Log-Euclidean/Log-Hilbert-Schmidt Distances between Positive Definite Operators.
Proceedings of the Geometric Science of Information - 4th International Conference, 2019
Covariances in Computer Vision and Machine Learning
Synthesis Lectures on Computer Vision, Morgan & Claypool Publishers, 2017
Log-Determinant Divergences Between Positive Definite Hilbert-Schmidt Operators.
Proceedings of the Geometric Science of Information - Third International Conference, 2017
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning.
J. Mach. Learn. Res., 2016
Infinite-dimensional Log-Determinant divergences II: Alpha-Beta divergences.
Kernel Methods on Approximate Infinite-Dimensional Covariance Operators for Image Classification.
Operator-Valued Bochner Theorem, Fourier Feature Maps for Operator-Valued Kernels, and Vector-Valued Learning.
Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016
Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015
Affine-Invariant Riemannian Distance Between Infinite-Dimensional Covariance Operators.
Proceedings of the Geometric Science of Information - Second International Conference, 2015
Log-Hilbert-Schmidt 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
Multivariate Slow Feature Analysis and Decorrelation Filtering for Blind Source Separation.
IEEE Trans. Image Process., 2013
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013
A unifying framework for vector-valued manifold regularization and multi-view 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
Semi-supervised multi-feature learning for person re-identification.
Proceedings of the 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013
Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference
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
A New Kernel-Based Approach for NonlinearSystem Identification.
IEEE Trans. Autom. Control., 2011
The regularized least squares algorithm and the problem of learning halfspaces.
Inf. Process. Lett., 2011
Vector-valued 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
Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces.
J. Math. Imaging Vis., 2010
Mercer's Theorem, Feature Maps, and Smoothing.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006
Learning Over Compact Metric Spaces.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004