Abstract
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.
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Kong, X., Zheng, C., Wu, Y., Wang, Y. (2008). Improving Tumor Clustering Based on Gene Selection. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC 2008. Lecture Notes in Computer Science, vol 5226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87442-3_6
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DOI: https://doi.org/10.1007/978-3-540-87442-3_6
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