Computational learning theory: an introduction
Computational learning theory: an introduction
The nature of statistical learning theory
The nature of statistical learning theory
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Holy Grail of Content-Based Media Analysis
IEEE MultiMedia
Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust minimax approach to classification
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge and Information Systems
Hidden space-based nonlinear discriminant feature extraction method
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Automatic colour-texture image segmentation using active contours
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Parameterization construction of integer wavelet transforms for embedded image coding
International Journal of Computer Mathematics - Computer Vision and Pattern Recognition
Robust foreground segmentation based on two effective background models
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Two-dimensional subspace classifiers for face recognition
Neurocomputing
Techniques for efficient and effective transformed image identification
Journal of Visual Communication and Image Representation
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
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
Constrained Laplacian Eigenmap for dimensionality reduction
Neurocomputing
Characteristic-based descriptors for motion sequence recognition
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Maximum margin criterion with tensor representation
Neurocomputing
Supervised learning of local projection kernels
Neurocomputing
Survey: Subspace methods for face recognition
Computer Science Review
Optimal calculation of tensor learning approaches
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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
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This paper aims to take general tensors as inputs for supervised learning. A supervised tensor learning (STL) framework is established for convex optimization based learning techniques such as support vector machines (SVM) and minimax probability machines (MPM). Within the STL framework, many conventional learning machines can be generalized to take n^th-order tensors as inputs. We also study the applications of tensors to learning machine design and feature extraction by linear discriminant analysis (LDA). Our method for tensor based feature extraction is named the tenor rank-one discriminant analysis (TR1DA). These generalized algorithms have several advantages: 1) reduce the curse of dimension problem in machine learning and data mining; 2) avoid the failure to converge; and 3) achieve better separation between the different categories of samples. As an example, we generalize MPM to its STL version, which is named the tensor MPM (TMPM). TMPM learns a series of tensor projections iteratively. It is then evaluated against the original MPM. Our experiments on a binary classification problem show that TMPM significantly outperforms the original MPM.