Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Matrix computations (3rd ed.)
An introduction to variable and feature selection
The Journal of Machine Learning Research
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
The Journal of Machine Learning Research
Document Clustering Using Locality Preserving Indexing
IEEE Transactions on Knowledge and Data Engineering
Novel Unsupervised Feature Filtering of Biological Data
Bioinformatics
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Rapid and brief communication: Laplacian linear discriminant analysis
Pattern Recognition
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Face recognition using kernel scatter-difference-based discriminant analysis
IEEE Transactions on Neural Networks
A Novel Hybrid Method of Gene Selection and Its Application on Tumor Classification
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
On the relevance of linear discriminative features
Information Sciences: an International Journal
Hybrid feature selection method for supervised classification based on Laplacian score ranking
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Gene Selection Using Iterative Feature Elimination Random Forests for Survival Outcomes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A novel multi-stage feature selection method for microarray expression data analysis
International Journal of Data Mining and Bioinformatics
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Until recently, numerous feature selection techniques have been proposed and found wide applications in genomics and proteomics. For instance, feature/gene selection has proven to be useful for biomarker discovery from microarray and mass spectrometry data. While supervised feature selection has been explored extensively, there are only a few unsupervised methods that can be applied to exploratory data analysis. In this paper, we address the problem of unsupervised feature selection. First, we extend Laplacian linear discriminant analysis (LLDA) to unsupervised cases. Second, we propose a novel algorithm for computing LLDA, which is efficient in the case of high dimensionality and small sample size as in microarray data. Finally, an unsupervised feature selection method, called LLDA-based Recursive Feature Elimination (LLDA-RFE), is proposed. We apply LLDA-RFE to several public data sets of cancer microarrays and compare its performance with those of Laplacian score and SVD-entropy, two state-of-the-art unsupervised methods, and with that of Fisher score, a supervised filter method. Our results demonstrate that LLDA-RFE outperforms Laplacian score and shows favorable performance against SVD-entropy. It performs even better than Fisher score for some of the data sets, despite the fact that LLDA-RFE is fully unsupervised.