Circuits, Communications and Systems, Pacific-Asia Conference on (2009)
May 16, 2009 to May 17, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/PACCS.2009.51
Due to the higher dimensional and nonlinear properties of the serial analysis of gene expression data, traditional self-organizing feature maps can't clustering effectively. To circumvent the parameters study of the self-organizing feature maps, a novel algorithm based on the Kalman filter and the unscented transform is presented. During the learning process, the learning coefficient and the width of the neighborhood function can updated automatically according to the input data. By clustering the mouse retinal SAGE data, results show that the novel algorithm has competence.
serial analysis of gene expression, kalman filter, self-organizing feature maps, unscented transform
Y. Zhu and H. Su, "UKF Based Self-organizing Feature Maps Algorithm for Serial Analysis of Gene Expression Data," 2009 Pacific-Asia Conference on Circuits, Communications and Systems (PACCS 2009)(PACCS), Chengdu, 2009, pp. 595-597.