Abstract
The studies of DNA Microarray technologies have produced high-dimensional data. In order to alleviate the “curse of dimensionality” and better analyze these data, many linear and non-linear dimension reduction methods such as PCA and LLE have been widely studied. In this paper, we report our work on microarray data classification with three latest proposed discriminant analysis methods: Locality Sensitive Discriminant Analysis (LSDA), Spectral Regression Discriminant Analysis (SRDA), and Supervised Neighborhood Preserving Embedding (S-NPE). Results of experiments on four data sets show the excellent effectiveness and efficiency of SRDA.
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References
He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood Preserving Embedding. In: Proc. of 10th IEEE Int. Conf. on Computer Vision, Beijing, China (2005)
Cai, D., He, X., Zhou, K., Han, J., Bao, H.: Locality Sensitive Discriminant Analysis. In: Proc. of 2007 Int. Joint Conf. on Artificial Intelligence, Hyderabad, India (2007)
Cai, D., He, X., Han, J.: SRDA: An Efficient Algorithm for Large Scale Discriminant Analysis. IEEE Transactions on Knowledge and Data Engineering 20, 1–12 (2008)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)
Van der Maaten, L.J.P.: An Introduction to Dimensionality Reduction Using Matlab. Report MICC 07-07. Maastricht University, Maastricht, the Netherlands (2007)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Chee, M., Yang, R., Hubbell, E., et al.: Accessing genetic information with high-density DNA arrays. Science 274, 610–614 (1996)
Schena, M., Shalon, D., Davis, R.W., et al.: Quantitative monitoring of gene expression patterns with a complementary DNA micro-array. Science 270, 467–470 (1995)
Raychaudhuri, S., Sutphinb, P.D., Changa, J.T., et al.: Basic microarray analysis: grouping and feature reduction. TRENDS in Biotechnology 19, 189–193 (2001)
Bowman, C., Aruliah, D., Fan, G., et al.: Nonlinear Dimension Reduction for Microarray Data (Small n and Large p). In: Proc. of the 1st Fields–MITACS Industrial Problems Workshop, Toronto, pp. 1–15 (2006)
Alter, O., Brown, P.O., Botstein, D.: Singular value decomposition for genome-wide expression data processing and modeling. Proc. of the National Academy of Sciences 97, 10101–10106 (2000)
Dai, J.J., Lieu, L., Rocke, D.: Dimension Reduction for Classification with Gene Expression Microarray Data. Statistical Applications in Genetics and Mol. Biol. 5, 1–15 (2006)
Shi, C., Chen, L.: Feature Dimension Reduction for Microarray Data Analysis Using Locally Linear Embedding. In: Proc. of APBC 2005, pp. 211–217 (2005)
Dawson, K., Rodriguez, R.L., Malyj, W.: Sample phenotype clusters in high-density oligonucleotide microarray data sets are revealed using Isomap, a nonlinear algorithm. BMC Bioinformatics 6 (2005)
Nilsson, J., Fioretos, T., Höglund, M., et al.: Approximate geodesic distances reveal biologically relevant structures in microarray data. Bioinformatics 20, 874–880 (2004)
Ye, J., Li, T., Tao, X., Janardan, R.: Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data. IEEE/ACM Trans. Comput. Biology Bioinform. 1, 181–190 (2004)
Cai, D., He, X., Han, J.: Training Linear Discriminant Analysis in Linear Time. In: Proc. 2008 Int. Conf. on Data Engineering (ICDE 2008), Cancun, Mexico (2008)
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Chen, C., Gong, YC., Bie, R. (2008). Discriminant Analysis Methods for Microarray Data Classification. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_26
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DOI: https://doi.org/10.1007/978-3-540-89378-3_26
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