Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Matrix-pattern-oriented Ho-Kashyap classifier with regularization learning
Pattern Recognition
Knowledge and Information Systems
Hierarchical Tensor Approximation of Multi-Dimensional Visual Data
IEEE Transactions on Visualization and Computer Graphics
A Tensor Approximation Approach to Dimensionality Reduction
International Journal of Computer Vision
Uncorrelated multilinear principal component analysis through successive variance maximization
Proceedings of the 25th international conference on Machine learning
Two heads better than one: pattern discovery in time-evolving multi-aspect data
Data Mining and Knowledge Discovery
Incremental tensor analysis: Theory and applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Appearance Models for Medical Volumes with Few Samples by Generalized 3D-PCA
Neural Information Processing
Shot-based video retrieval with optical flow tensor and HMMs
Pattern Recognition Letters
Discriminative optical flow tensor for video semantic analysis
Computer Vision and Image Understanding
Tensor linear Laplacian discrimination (TLLD) for feature extraction
Pattern Recognition
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
IEEE Transactions on Neural Networks
Bidirectional visible neighborhood preserving embedding
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Maximum margin criterion with tensor representation
Neurocomputing
Generalized low-rank approximations of matrices revisited
IEEE Transactions on Neural Networks
Feature extraction by learning Lorentzian metric tensor and its extensions
Pattern Recognition
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
Optimum subspace learning and error correction for tensors
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Tensor-based locally maximum margin classifier for image and video classification
Computer Vision and Image Understanding
Discriminative concept factorization for data representation
Neurocomputing
Empirical discriminative tensor analysis for crime forecasting
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Color face tensor factorization and slicing for illumination-robust recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Face recognition by discriminant analysis with gabor tensor representation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Summarizing dynamic Social Tagging Systems
Expert Systems with Applications: An International Journal
Thinking of images as what they are: compound matrix regression for image classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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A representative subspace is significant for image analysis, while the corresponding techniques often suffer from the curse of dimensionality dilemma. In this paper, we propose a new algorithm, called Concurrent Subspaces Analysis (CSA), to derive representative subspaces by encoding image objects as 2nd or even higher order tensors. In CSA, an original higher dimensional tensor is transformed into a lower dimensional one using multiple concurrent subspaces that characterize the most representative information of different dimensions, respectively. Moreover, an efficient procedure is provided to learn these subspaces in an iterative manner. As analyzed in this paper, each sub-step of CSA takes the column vectors of the matrices, which are acquired from the k-mode unfolding of the tensors, as the new objects to be analyzed, thus the curse of dimensionality dilemma can be effectively avoided. The extensive experiments on the 3rd order tensor data, simulated video sequences and Gabor filtered digital number image database show that CSA outperforms Principal Component Analysis in terms of both reconstruction and classification capability.