Matrix-pattern-oriented Ho-Kashyap classifier with regularization learning

Authors:
Songcan Chen;Zhe Wang;Yongjun Tian
Affiliations:
Department of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China;Department of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China;Department of Computer Science & Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
Venue:
Pattern Recognition
Year:
2007

Citing 16
Cited 4

Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Adaptive regularization parameter selection method for enhancing generalization capability of neural networks

Artificial Intelligence
Optimal design of regularization term and regularization parameter by subspace information criterion

Neural Networks
Ho--Kashyap classifier with generalization control

Pattern Recognition Letters
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
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
Coupled Kernel-Based Subspace Learning

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Concurrent Subspaces Analysis

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
A Framework of 2D Fisher Discriminant Analysis: Application to Face Recognition with Small Number of Training Samples

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
Design of Multicategory Pattern Classifiers with Two-Category Classifier Design Procedures

IEEE Transactions on Computers
2D-LDA: A statistical linear discriminant analysis for image matrix

Pattern Recognition Letters
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

A novel multi-view learning developed from single-view patterns

Pattern Recognition
Regularized multi-view learning machine based on response surface technique

Neurocomputing
Three-fold structured classifier design based on matrix pattern

Pattern Recognition
Multiple rank multi-linear SVM for matrix data classification

Pattern Recognition

Quantified Score

Hi-index	0.01

Visualization

Abstract

Existing classifier designs generally base on vector pattern, hence, when a non-vector pattern such as a face image as the input to the classifier, it has to be first concatenated to a vector. In this paper, we, instead, explore using a set of given matrix patterns to design a classifier. For this, first we represent a pattern in matrix form and recast existing vector-based classifiers to their corresponding matrixized versions and then optimize their parameters. Concretely, considering its similar principle to the support vector machines of maximizing the separation margin and superior generalization performance, the modified HK algorithm (MHKS) is chosen and then a matrix-based MHKS classifier (MatMHKS) is developed. Experimental results on ORL, Letters and UCI data sets show that MatMHKS is more powerful in generalization than MHKS. This paper focuses on: (1) purely exploring the classification performance discrepancy between matrix- and vector-pattern representations; more importantly, (2) developing a new classifier design directly for matrix pattern.