Abstract
In this paper we propose a new method for finding a low dimensional subspace of high dimensional microarray data. We developed a new criterion for constructing the weight matrix by using local neighborhood information to discover the intrinsic discriminant structure in the data. Also this approach applies regularized least square technique to extract relevant features. We assess the performance of the proposed methodology by applying it to four publicly available tumor datasets. In a low dimensional subspace, the proposed method classified these tumors accurately and reliably. Also, through a comparison study, the reliability of the dimensionality reduction and discrimination results is verified.
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Cui, Y., Zheng, CH., Yang, J. (2013). Dimensionality Reduction for Microarray Data Using Local Mean Based Discriminant Analysis. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_31
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DOI: https://doi.org/10.1007/978-3-642-39482-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39481-2
Online ISBN: 978-3-642-39482-9
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