Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization
ICML '06 Proceedings of the 23rd international conference on Machine learning
Classification of microarrays to nearest centroids
Bioinformatics
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Face recognition based on multi-class mapping of Fisher scores
Pattern Recognition
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Bayesian compressive sensing using Laplace priors
IEEE Transactions on Image Processing
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularization in matrix relevance learning
IEEE Transactions on Neural Networks
Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Correlated Biclusters from Gene Expression Data
IEEE Transactions on Knowledge and Data Engineering
Global Solutions of Variational Models with Convex Regularization
SIAM Journal on Imaging Sciences
Face Recognition by Regularized Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Solving Nonstationary Classification Problems With Coupled Support Vector Machines
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
A novel classification method using @?"2","1-norm based regression is proposed in this paper. The @?"2","1-norm based loss function is robust to outliers or large variations distributed in the given data, and the @?"2","1-norm regularization term selects correlated samples across the whole training set with grouped sparsity. A probabilistic interpretation under the multiple task learning framework presents theoretical foundation for the optimal solution. Complexity analysis of our proposed classification algorithm is also presented. Several benchmark data sets including facial images and gene expression data are used for evaluating the effectiveness of the new proposed algorithm, and the results show competitive performance particularly better than those using dummy matrix as the response variables. This result is very useful since it is important for selecting appropriate response variables in classification oriented regression models.