Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Real-Time Face Recognition Using Gram-Schmidt Orthogonalization for LDA
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Multi-label informed latent semantic indexing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-labelled classification using maximum entropy method
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of two systems on multi-class multi-label document classification
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-label linear discriminant analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Hierarchical classification of web documents by stratified discriminant analysis
IRFC'12 Proceedings of the 5th conference on Multidisciplinary Information Retrieval
Pattern Recognition Letters
| Hi-index | 0.10 |
Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, but it is originally focused on a single-labeled problem. In this paper, we derive the formulation for applying LDA for a multi-labeled problem. We also propose a generalized LDA algorithm which is effective in a high dimensional multi-labeled problem. Experimental results demonstrate that by considering multi-labeled structure, LDA can achieve computational efficiency and also improve classification performances.