Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
A new discriminative kernel from probabilistic models
Neural Computation
Face Recognition from Long-Term Observations
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Text classification using string kernels
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Comparison of face matching techniques under pose variation
Proceedings of the 6th ACM international conference on Image and video retrieval
Probabilistic distance measures of the Dirichlet and Beta distributions
Pattern Recognition
A kernel optimization method based on the localized kernel Fisher criterion
Pattern Recognition
Grassmann discriminant analysis: a unifying view on subspace-based learning
Proceedings of the 25th international conference on Machine learning
International Journal of Knowledge Engineering and Soft Data Paradigms
Video retrieval based on object discovery
Computer Vision and Image Understanding
A novel kernel-based maximum a posteriori classification method
Neural Networks
An MRF-based kernel method for nonlinear feature extraction
Image and Vision Computing
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Graph-based classification of multiple observation sets
Pattern Recognition
Framelet kernels with applications to support vector regression and regularization networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Shot retrieval based on fuzzy evolutionary aiNet and hybrid features
Computers in Human Behavior
Video-based face recognition: state of the art
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Deriving kernels from generalized Dirichlet mixture models and applications
Information Processing and Management: an International Journal
Colbar: A collaborative location-based regularization framework for QoS prediction
Information Sciences: an International Journal
| Hi-index | 0.14 |
This paper addresses the problem of characterizing ensemble similarity from sample similarity in a principled manner. Using reproducing kernel as a characterization of sample similarity, we suggest a probabilistic distance measure in the reproducing kernel Hilbert space (RKHS) as the ensemble similarity. Assuming normality in the RKHS, we derive analytic expressions for probabilistic distance measures that are commonly used in many applications, such as Chernoff distance (or the Bhattacharyya distance as its special case), Kullback-Leibler divergence, etc. Since the reproducing kernel implicitly embeds a nonlinear mapping, our approach presents a new way to study these distances whose feasibility and efficiency is demonstrated using experiments with synthetic and real examples. Further, we extend the ensemble similarity to the reproducing kernel for ensemble and study the ensemble similarity for more general data representations.