Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
A new combined filter-wrapper framework for gene subset selection with specialized genetic operators
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
A hybrid system for distortion classification and image quality evaluation
Image Communication
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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DNA microarray technology can monitor thousands of genes in a single experiment. One important application of this high-throughput gene expression data is to classify samples into known categories. Since the number of gene often exceeds the number of samples, classical classification methods do not work well under this circumstance. Furthermore, there are many irrelevant and redundant genes which will decrease classification accuracy, thus a gene selection process is necessary. More accurate classification result using these selected genes is expected. A novel informative gene selection and sample classification method for gene expression data is proposed in this paper. This method is based on Linear Discriminant Analysis (LDA) in the regular space and the null space of within-class scatter matrix. By recursively filtering genes which have smaller coefficient in the optimal projection basis vectors, the remaining genes are more and more informative. The results of experiments on leukemia dataset and the colon dataset show that genes in this subset have much less correlations and more discriminative power compared to those selected by classical methods.