Matrix analysis
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Learning low-rank kernel matrices for constrained clustering
Neurocomputing
Expert Systems with Applications: An International Journal
Graph optimization for dimensionality reduction with sparsity constraints
Pattern Recognition
Robust linearly optimized discriminant analysis
Neurocomputing
Incremental complete LDA for face recognition
Pattern Recognition
Large-scale Structure-from-Motion Reconstruction with small memory consumption
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
A Rayleigh-Ritz style method for large-scale discriminant analysis
Pattern Recognition
Hi-index | 0.00 |
Dimensionality reduction is an important issue in many machine learning and pattern recognition applications, and the trace ratio (TR) problem is an optimization problem involved in many dimensionality reduction algorithms. Conventionally, the solution is approximated via generalized eigenvalue decomposition due to the difficulty of the original problem. However, prior works have indicated that it is more reasonable to solve it directly than via the conventional way. In this brief, we propose a theoretical overview of the global optimum solution to the TR problem via the equivalent trace difference problem. Eigenvalue perturbation theory is introduced to derive an efficient algorithm based on the Newton-Raphson method. Theoretical issues on the convergence and efficiency of our algorithm compared with prior literature are proposed, and are further supported by extensive empirical results.