Abstract
It is of great importance to classify the gene expression data into different classes. In this paper, followed the assumption that the gene expression data of tumor may be sampled from the data with a probability distribution on a sub-manifold of ambient space, an efficient feature extraction method named as Constrained Maximum Variance Mapping (CMVM), is presented for tumor classification. The proposed algorithm can be viewed as a linear approximation of multi-manifolds learning based approach, which takes the local geometry and manifold labels into account. The proposed CMVM method was tested on four DNA microarray datasets, and the experimental results demonstrated that it is efficient for tumor classification.
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Zheng, CH., Wu, FL., Li, B., Wang, J. (2009). Constrained Maximum Variance Mapping for Tumor Classification. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_12
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DOI: https://doi.org/10.1007/978-3-642-04070-2_12
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