Active shape models—their training and application
Computer Vision and Image Understanding
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
Journal of Cognitive Neuroscience
Enhancement and detection of lung nodules with Multiscale filters in CT images
IIH-MSP '08 Proceedings of the 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Probabilistic learning of similarity measures for tensor PCA
Pattern Recognition Letters
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We propose a method called generalized N-dimensional principal component analysis (GND-PCA) for the modeling of a series of multi-dimensional data in this paper. In this method, the data are directly trained as the higher-order tensor and the bases in each mode subspace are calculated to compactly represent the data. Since GND-PCA analyzes the multi-dimensional data directly on each mode subspace rather than the unfolded 1D vector space, it can not only be calculated efficiently but also have better performance on generalization than PCA. Additionally, since GND-PCA can compress the data in each mode subspace, it can represent the data more efficiently, compared to the recently proposed ND-PCA method. We apply the proposed GND-PCA method to construct the appearance models for 18 MR T1-weighted brain volumes and 25 CT lung volumes, respectively. The leave-one-out experiments show that the statistical appearance models built by our method can represent an untrained data well even though the models are trained by fewer samples.