Matrix-pattern-oriented Ho-Kashyap classifier with regularization learning
Pattern Recognition
New Least Squares Support Vector Machines Based on Matrix Patterns
Neural Processing Letters
A Tensor Approximation Approach to Dimensionality Reduction
International Journal of Computer Vision
Matrix-pattern-oriented least squares support vector classifier with AdaBoost
Pattern Recognition Letters
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Object trajectory clustering via tensor analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Generalized low-rank approximations of matrices revisited
IEEE Transactions on Neural Networks
A new implementation of common matrix approach using third-order tensors for face recognition
Expert Systems with Applications: An International Journal
Incremental Tensor Subspace Learning and Its Applications to Foreground Segmentation and Tracking
International Journal of Computer Vision
Three-fold structured classifier design based on matrix pattern
Pattern Recognition
A tensor factorization based least squares support tensor machine for classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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
Tensor clustering via adaptive subspace iteration
Intelligent Data Analysis
| Hi-index | 0.00 |
We present a novel multilinear algebra based approach for reduced dimensionality representation of image ensembles. We treat an image as a matrix, instead of a vector as in traditional dimensionality reduction techniques like PCA, and higher-dimensional data as a tensor. This helps exploit spatio-temporal redundancies with less information loss than image-as-vector methods. The challenges lie in the computational and memory requirements for large ensembles. Currently, there exists a rank-R approximation algorithm which, although applicable to any number of dimensions, isefficient for only low-rank approximations. For larger dimensionality reductions, the memory and time costs of this algorithm become prohibitive. We propose a novel algorithm for rank-R approximations of third-order tensors, which is efficient for arbitrary R but for the important special case of 2D image ensembles, e.g. video. Both of these algorithms reduce redundancies present in all dimensions. Rank-R tensor approximation yields the most compact data representation among all known image-as-matrix methods. We evaluated the performance of our algorithm vs. other approaches on a number of datasets with the following two main results. First, for a fixed compression ratio, the proposed algorithm yields the best representation of image ensembles visually as well as in the least squares sense. Second, proposed representation gives the best performance for object classification.