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Abstract
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, a supervised version of locally linear embedding (LLE), named locally linear discriminant embedding (LLDE), is proposed for tumor classification. In the proposed algorithm, we construct a vector translation and distance rescaling model to enhance the recognition ability of the original LLE from two aspects. To validate the efficiency, the proposed method is applied to classify two different DNA microarray datasets. The prediction results show that our method is efficient and feasible.
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Authors and Affiliations
College of Information and Communication Technology, Qufu Normal University, Rizhao, Shandong, 276826, China
Chun-Hou Zheng
Intelligent Computing Lab, Institute of Intelligent Machines, Chinese Academy of, Sciences, Hefei, Anhui, 230031, China
Chun-Hou Zheng, Bo Li & Hong-Qiang Wang
Biometric Research Center, Dept. of Computing, Hong Kong Polytechnic University, Hong Kong, China
Lei Zhang
- Chun-Hou Zheng
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- Bo Li
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- Lei Zhang
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- Hong-Qiang Wang
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Zheng, CH., Li, B., Zhang, L., Wang, HQ. (2008). Locally Linear Discriminant Embedding for Tumor Classification. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_131
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