Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Inverse Free Preconditioned Krylov Subspace Method for Symmetric Generalized Eigenvalue Problems
SIAM Journal on Scientific Computing
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Face Recognition Using Temporal Image Sequence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Semantic Spaces: Measuring the Distance between Different Subspaces
QI '09 Proceedings of the 3rd International Symposium on Quantum Interaction
Single image blind deconvolution with higher-order texture statistics
Proceedings of the 2010 international conference on Video Processing and Computational Video
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As pattern recognition methods, subspace methods have attracted much attention in the fields of face, object and video-based recognition in recent years. In subspace methods, each instance is characterized by a subspace that is spanned by a set of vectors. Thus, the distance between instances reduces to the distance between subspaces. Herein, the subspace distance designing problem is considered mathematically. Any distance designed according the method presented here can be embedded into associated recognition algorithms. The main contributions in this paper include: – Solving the open problem proposed by Wang, Wang and Feng (2005), that is, we proved that their dissimilarity is a distance; – Presenting a general framework of subspace construction, concretely speaking, we pointed out a view that subspace distance also could be regarded as the classical distance in vector space; – Proposing two types of kernel subspace distances; – Comparing some known subspace (dis)similarities mathematically.