Antialiasing through stochastic sampling
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Seismic Denoising with Nonuniformly Sampled Curvelets
Computing in Science and Engineering
Feature Subset Selection and Ranking for Data Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit
Foundations of Computational Mathematics
Classification via group sparsity promoting regularization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Block-sparsity: Coherence and efficient recovery
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Boosted online learning for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Random Projection RBF Nets for Multidimensional Density Estimation
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
Improved Group Sparse Classifier
Pattern Recognition Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Additive Support Vector Machines for Pattern Classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face Recognition by Regularized Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Linear Discriminant Analysis for Face Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discriminant Learning Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Linear demosaicing inspired by the human visual system
IEEE Transactions on Image Processing
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
| Hi-index | 0.00 |
The computational cost for most classification algorithms is dependent on the dimensionality of the input samples. As the dimensionality could be high in many cases, particularly those associated with image classification, reducing the dimensionality of the data becomes a necessity. The traditional dimensionality reduction methods are data dependent, which poses certain practical problems. Random projection (RP) is an alternative dimensionality reduction method that is data independent and bypasses these problems. The nearest neighbor classifier has been used with the RP method in classification problems. To obtain higher recognition accuracy, this study looks at the robustness of RP dimensionality reduction for several recently proposed classifiers--sparse classifier (SC), group SC (along with their fast versions), and the nearest subspace classifier. Theoretical proofs are offered regarding the robustness of these classifiers to RP. The theoretical results are confirmed by experimental evaluations.