The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisurface Proximal Support Vector Machine Classification via Generalized Eigenvalues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonsmooth Optimization Techniques for Semisupervised Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Techniques for Semi-Supervised Support Vector Machines
The Journal of Machine Learning Research
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Linear Neighborhood Propagation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Semi-supervised bilinear subspace learning
IEEE Transactions on Image Processing
IEEE Transactions on Neural Networks
Multi-weight vector projection support vector machines
Pattern Recognition Letters
Learning a tensor subspace for semi-supervised dimensionality reduction
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Interactive Image Segmentation via Adaptive Weighted Distances
IEEE Transactions on Image Processing
Image Classification Using Correlation Tensor Analysis
IEEE Transactions on Image Processing
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Comments on “Efficient and Robust Feature Extraction by Maximum Margin Criterion”
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Multiple rank multi-linear SVM for matrix data classification
Pattern Recognition
| Hi-index | 12.05 |
Virtually all previous classifier models take vectors as inputs, performing directly based on the vector patterns. But it is highly necessary to consider images as matrices in real applications. In this paper, we represent images as second order tensors or matrices. We then propose two novel tensor algorithms, which are referred to as Maximum Margin Multisurface Proximal Support Tensor Machine (M^3PSTM) and Maximum Margin Multi-weight Vector Projection Support Tensor Machine (M^3VSTM), for classifying and segmenting the images. M^3PSTM and M^3VSTM operate in tensor space and aim at computing two proximal tensor planes for multisurface learning. To avoid the singularity problem, maximum margin criterion is used for formulating the optimization problems. Thus the proposed tensor classifiers have an analytic form of projection axes and can achieve the maximum margin representations for classification. With tensor representation, the number of estimated parameters is significantly reduced, which makes M^3PSTM and M^3VSTM more computationally efficient when handing the high-dimensional datasets than applying the vector representations based methods. Thorough image classification and segmentation simulations on the benchmark UCI and real datasets verify the efficiency and validity of our approaches. The visual and numerical results show M^3PSTM and M^3VSTM deliver comparable or even better performance than some state-of-the-art classification algorithms.