Locally linear embedding

Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification

L. Sun, Xu, J. - C., Wang, W., Yin, Y., Sun, L., Xu, J. - C., Wang, W., and Yin, Y., Locally linear embedding and neighborhood rough set-based gene selection for gene expression data classification, vol. 15, p. -, 2016.

Cancer subtype recognition and feature selection are important problems in the diagnosis and treatment of tumors. Here, we propose a novel gene selection approach applied to gene expression data classification. First, two classical feature reduction methods including locally linear embedding (LLE) and rough set (RS) are summarized. The advantages and disadvantages of these algorithms were analyzed and an optimized model for tumor gene selection was developed based on LLE and neighborhood RS (NRS).

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