IEEE Transactions on Pattern Analysis and Machine Intelligence
Offline Recognition of Chinese Handwriting by Multifeature and Multilevel Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Recognition of Handwritten Numerals Using Gabor Features
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Convex Optimization
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Subclass Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hypergraph spectral learning for multi-label classification
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A scalable two-stage approach for a class of dimensionality reduction techniques
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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Mixture discriminant analysis (MDA) and subclass discriminant analysis (SDA) belong to the supervised classification approaches. They have advantage over the standard linear discriminant analysis (LDA) in large sample size problems, since both of them divide the samples in each class into subclasses which keep locality but LDA does not. However, since the current MDA and SDA algorithms perform subclass division in just one step in the original data space before solving the generalized eigenvalue problem, two problems are exposed: (1) they ignore the relation among classes since subclass division is performed in each isolated class; (2) they cannot guarantee good performance of classifiers in the transformed space, because locality in the original data space may not be kept in the transformed space. To address these problems, this paper presents a new approach for subclass division based on k-means clustering in the projected space, class by class using the iterative steps under EM-alike framework. Experiments are performed on the artificial data set, the UCI machine learning data sets, the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, and the terrain cover database. Extensive experimental results demonstrate the performance advantages of the proposed method.