Independent component analysis: algorithms and applications
Neural Networks
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Molecular cancer class discovery using non-negative matrix factorization with sparseness constraint
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
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Tumor clustering is becoming a powerful method in cancer class discovery. In this community, non-negative matrix factorization (NMF) has shown its advantages, such as the accuracy and robustness of the representation, over other conventional clustering techniques. Though NMF has shown its efficiency in tumor clustering, there is a considerable room for improvement in clustering accuracy and robustness. In this paper, gene selection and explicitly enforcing sparseness are introduced into clustering process. The independent component analysis (ICA) is employed to select a subset of genes. The unsupervised methods NMF and its extensions, sparse NMF (SNMF) and NMF with sparseness constraint (NMFSC), are then used for tumor clustering on the subset of genes selected by ICA. The experimental results demonstrate the efficiency of the proposed scheme.