Conceptual Modeling of Coincident Failures in Multiversion Software
IEEE Transactions on Software Engineering
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Subwindows for Robust Image Classification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Rank-R Approximation of Tensors: Using Image-as-Matrix Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Generalized Low Rank Approximations of Matrices
Machine Learning
Non-iterative generalized low rank approximation of matrices
Pattern Recognition Letters
New Least Squares Support Vector Machines Based on Matrix Patterns
Neural Processing Letters
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Texture descriptors for generic pattern classification problems
Expert Systems with Applications: An International Journal
A novel multi-view learning developed from single-view patterns
Pattern Recognition
Matrix pattern based minimum within-class scatter support vector machines
Applied Soft Computing
Matrix representation in pattern classification
Expert Systems with Applications: An International Journal
Combining heterogeneous classifiers for relational databases
Pattern Recognition
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Matrix-pattern-oriented least squares support vector classifier (MatLSSVC) can directly classify matrix patterns and has a superior classification performance than its vector version least squares support vector classifier (LSSVC) especially for images. However, it can be found that the classification performance of MatLSSVC is matrixization-dependent, i.e. heavily relying on the reshaping ways from the original (vector or matrix) pattern to (another) matrix. Thus, it is difficult to determine which reshaping way is fittest to classification. On the other hand, the changeable and different reshaping ways can naturally give birth to a set of MatLSSVCs with diversity and it is the diversity that provides a means to build an ensemble of classifiers. In this paper, we exactly exploit the diversity of the changeable reshaping ways and borrow AdaBoost to construct an AdaBoost-MatLSSVC ensemble named AdaMatLSSVC. Our contributions are that: (1) the proposed AdaMatLSSVC can greatly avoid the matrixization-dependent problem on single MatLSSVC; (2) different from the ensemble principle of the original AdaBoost that uses a single type of classifiers as its base components, the proposed AdaMatLSSVC is on top of multiple types of MatLSSVCs in different reshapings; (3) since AdaMatLSSVC adopts multiple matrix representations of the same pattern, it can provide a complementarity among different (matrix) representation spaces; (4) AdaMatLSSVC mitigates the selection of the regularization parameter, which are all validated in the experiments here.