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
A Comparative Study of Linear and Nonlinear Feature Extraction Methods
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic pattern extraction of parameters in laser welding process
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
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Traditional classification problem assumes that a data sample belongs to one class among the predefined classes. On the other hand, in a multi-labeled problem such as text categorization, data samples can belong to multiple classes and the task is to output a set of class labels associated with new unseen data sample. As common in text categorization problem, learning a classifier in a high dimensional space can be difficult, known as the curse of dimensionality. It has been shown that performing dimension reduction as a preprocessing step can improve classification performances greatly. Especially, Linear discriminant analysis (LDA) is one of the most popular dimension reduction methods, which is optimized for classification tasks. However, in applying LDA for a multi-labeled problem some ambiguities and difficulties can arise. In this paper, we study on applying LDA for a multi-labeled problem and analyze how an objective function of LDA can be interpreted in multi-labeled setting. We also propose a LDA algorithm which is effective in a multi-labeled problem. Experimental results demonstrate that by considering multi-labeled structures LDA can achieve computational efficiency and also improve classification performances greatly.