Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Generalized low rank approximations of matrices
ICML '04 Proceedings of the twenty-first international conference on Machine learning
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Out-of-core tensor approximation of multi-dimensional matrices of visual data
ACM SIGGRAPH 2005 Papers
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Multilinear Tensor-Based Non-parametric Dimension Reduction for Gait Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Tensor distance based multilinear multidimensional scaling for image and video analysis
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Locating nose-tips and estimating head poses in images by tensorposes
IEEE Transactions on Circuits and Systems for Video Technology
Tensor-based transductive learning for multimodality video semantic concept detection
IEEE Transactions on Multimedia
IEEE Transactions on Neural Networks
Bidirectional visible neighborhood preserving embedding
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Distance approximating dimension reduction of Riemannian manifolds
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Low-resolution gait recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Supervised manifold learning for image and video classification
Proceedings of the international conference on Multimedia
Tensor distance based multilinear locality-preserved maximum information embedding
IEEE Transactions on Neural Networks
Tensor-based locally maximum margin classifier for image and video classification
Computer Vision and Image Understanding
A survey of multilinear subspace learning for tensor data
Pattern Recognition
Expert Systems with Applications: An International Journal
Tensor rank one differential graph preserving analysis for facial expression recognition
Image and Vision Computing
Sparse tensor embedding based multispectral face recognition
Neurocomputing
| Hi-index | 0.00 |
Over the past few years, some embedding methods have been proposed for feature extraction and dimensionality reduction in various machine learning and pattern classification tasks. Among the methods proposed are Neighborhood Preserving Embedding (NPE), Locality Preserving Projection (LPP) and Local Discriminant Embedding (LDE) which have been used in such applications as face recognition and image/video retrieval. However, although the data in these applications are more naturally represented as higher-order tensors, the embedding methods can only work with vectorized data representations which may not capture well some useful information in the original data. Moreover, high-dimensional vectorized representations also suffer from the curse of dimensionality and the high computational demand. In this paper, we propose some novel tensor embedding methods which, unlike previous methods, take data directly in the form of tensors of arbitrary order as input. These methods allow the relationships between dimensions of a tensor representation to be efficiently characterized. Moreover, they also allow the intrinsic local geometric and topological properties of the manifold embedded in a tensor space to be naturally estimated. Furthermore, they do not suffer from the curse of dimensionality and the high computational demand. We demonstrate the effectiveness of the proposed tensor embedding methods on a face recognition application and compare them with some previous methods. Extensive experiments show that our methods are not only more effective but also more efficient.