Learning Log-Determinant Divergences for Positive Definite Matrices
暂无分享,去创建一个
Vassilios Morellas | Anoop Cherian | Panagiotis Stanitsas | Nikolaos Papanikolopoulos | Mehrtash Harandi | Jue Wang | N. Papanikolopoulos | V. Morellas | A. Cherian | P. Stanitsas | Mehrtash Harandi | Jue Wang
[1] J. Lafferty. Additive models, boosting, and inference for generalized divergences , 1999, COLT '99.
[2] Suvrit Sra,et al. Geometric Mean Metric Learning , 2016, ICML.
[3] Mehrtash Tafazzoli Harandi,et al. Riemannian coding and dictionary learning: Kernels to the rescue , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Anoop Cherian,et al. Second-order Temporal Pooling for Action Recognition , 2017, International Journal of Computer Vision.
[5] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[6] Cristian Sminchisescu,et al. Matrix Backpropagation for Deep Networks with Structured Layers , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[7] Baba C. Vemuri,et al. On A Nonlinear Generalization of Sparse Coding and Dictionary Learning , 2013, ICML.
[8] Bruno Pelletier. Kernel density estimation on Riemannian manifolds , 2005 .
[9] Robert H. Halstead,et al. Matrix Computations , 2011, Encyclopedia of Parallel Computing.
[10] Shiguang Shan,et al. Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification , 2015, ICML.
[11] Inderjit S. Dhillon,et al. Learning low-rank kernel matrices , 2006, ICML.
[12] René Vidal,et al. Clustering and dimensionality reduction on Riemannian manifolds , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[13] Luc Van Gool,et al. A Riemannian Network for SPD Matrix Learning , 2016, AAAI.
[14] S. Luo,et al. Informational distance on quantum-state space , 2004 .
[15] Cordelia Schmid,et al. Towards Understanding Action Recognition , 2013, 2013 IEEE International Conference on Computer Vision.
[16] Charles Blundell,et al. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles , 2016, NIPS.
[17] Vassilios Morellas,et al. Metric learning for semi-supervised clustering of Region Covariance Descriptors , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).
[18] Vassilios Morellas,et al. Evaluation of feature descriptors for cancerous tissue recognition , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[19] Anoop Cherian,et al. Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[20] Yang Gao,et al. Compact Bilinear Pooling , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Sergio Cruces,et al. Log-Determinant Divergences Revisited: Alpha-Beta and Gamma Log-Det Divergences , 2014, Entropy.
[22] Duc Fehr. Covariance Based Point Cloud Descriptors for Object Detection and Classification , 2013 .
[23] Raul Kompass,et al. A Generalized Divergence Measure for Nonnegative Matrix Factorization , 2007, Neural Computation.
[24] Peter Meer,et al. Nonlinear Mean Shift over Riemannian Manifolds , 2009, International Journal of Computer Vision.
[25] Mihoko Minami,et al. Robust Blind Source Separation by Beta Divergence , 2002, Neural Computation.
[26] Anoop Cherian,et al. Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Sergio Cruces,et al. Optimization of Alpha-Beta Log-Det Divergences and their Application in the Spatial Filtering of Two Class Motor Imagery Movements , 2017, Entropy.
[28] R. Bhatia,et al. On the Bures–Wasserstein distance between positive definite matrices , 2017, Expositiones Mathematicae.
[29] Vassilios Morellas,et al. Bayesian Nonparametric Clustering for Positive Definite Matrices , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Xavier Pennec,et al. A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.
[31] Thomas Serre,et al. HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.
[32] Fatih Murat Porikli,et al. Region Covariance: A Fast Descriptor for Detection and Classification , 2006, ECCV.
[33] René Vidal,et al. Kernel sparse subspace clustering , 2014, 2014 IEEE International Conference on Image Processing (ICIP).
[34] Maher Moakher,et al. Symmetric Positive-Definite Matrices: From Geometry to Applications and Visualization , 2006, Visualization and Processing of Tensor Fields.
[35] G. Borgefors,et al. Segmentation of virus particle candidates in transmission electron microscopy images , 2012, Journal of microscopy.
[36] Andrew Zisserman,et al. Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.
[37] Mehrtash Harandi,et al. Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Dieter Fox,et al. A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.
[39] Junbin Gao,et al. Kernel Sparse Subspace Clustering on Symmetric Positive Definite Manifolds , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[40] Andrzej Cichocki,et al. Nonnegative Matrix and Tensor Factorization T , 2007 .
[41] Mehrtash Tafazzoli Harandi,et al. Bregman Divergences for Infinite Dimensional Covariance Matrices , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[42] Erkki Oja,et al. Learning the Information Divergence , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Inderjit S. Dhillon,et al. Generalized Nonnegative Matrix Approximations with Bregman Divergences , 2005, NIPS.
[44] H. Minh,et al. Alpha-Beta Log-Determinant Divergences Between Positive Definite Trace Class Operators , 2019, Information Geometry.
[45] Vassilios Morellas,et al. Clustering Positive Definite Matrices by Learning Information Divergences , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).
[46] Masashi Sugiyama,et al. Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).
[47] N. Ayache,et al. Log‐Euclidean metrics for fast and simple calculus on diffusion tensors , 2006, Magnetic resonance in medicine.
[48] Mathieu Salzmann,et al. Statistically Motivated Second Order Pooling , 2018, ECCV.
[49] Mikhail Belkin,et al. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.
[50] Ali Taylan Cemgil,et al. Learning mixed divergences in coupled matrix and tensor factorization models , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[51] Andrzej Cichocki,et al. Families of Alpha- Beta- and Gamma- Divergences: Flexible and Robust Measures of Similarities , 2010, Entropy.
[52] Matti Pietikäinen,et al. A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..
[53] A. Bhattacharyya. On a measure of divergence between two statistical populations defined by their probability distributions , 1943 .
[54] Lei Zhang,et al. Log-Euclidean Kernels for Sparse Representation and Dictionary Learning , 2013, 2013 IEEE International Conference on Computer Vision.
[55] Levent Tunçel,et al. Optimization algorithms on matrix manifolds , 2009, Math. Comput..
[56] Suvrit Sra,et al. A new metric on the manifold of kernel matrices with application to matrix geometric means , 2012, NIPS.
[57] Zhizhou Wang,et al. An affine invariant tensor dissimilarity measure and its applications to tensor-valued image segmentation , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..
[58] Larry S. Davis,et al. Covariance discriminative learning: A natural and efficient approach to image set classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[59] Vittorio Murino,et al. Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces , 2014, NIPS.
[60] Dario Bini,et al. Computing the Karcher mean of symmetric positive definite matrices , 2013 .
[61] Vassilios Morellas,et al. Tensor Sparse Coding for Region Covariances , 2010, ECCV.
[62] Subhransu Maji,et al. Second-order Democratic Aggregation , 2018, ECCV.
[63] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[64] Anoop Cherian,et al. Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).
[65] Geoffrey E. Hinton,et al. Stochastic Neighbor Embedding , 2002, NIPS.
[66] Vassilios Morellas,et al. Learning Discriminative αβ-Divergences for Positive Definite Matrices , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[67] José Mario Martínez,et al. Algorithm 813: SPG—Software for Convex-Constrained Optimization , 2001, TOMS.
[68] M. C. Jones,et al. Robust and efficient estimation by minimising a density power divergence , 1998 .
[69] Thomas Brox,et al. Nonlinear structure tensors , 2006, Image Vis. Comput..