Abstract
The exponential increase of data in health informatics has brought a lot of challenges in terms of data transfer, storage, computation and analysis. One of the popular solutions to the above challenges is the cloud computing technology. However, the cloud computing technology requires high-performance computers and is only accessible with internet. In this paper, we introduce online learning and propose our method for data mining of big data in health informatics. In contrast to traditional data analysis scenario, online learning will preform the data analysis dynamically by the time the data are generated. The online learning method is efficient and especially adaptable to the online health care systems. We demonstrate the effectiveness of our online learning method on several real-world data sets.
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References
Chae, H., Jung, I., Lee, H., Marru, S., Lee, S.W., Kim, S.: Bio and health informatics meets cloud: Biovlab as an example. Health Information Science and Systems 1(1), 6 (2013)
Goldberg, L., Lide, B., Lowry, S., Massett, H.A., O’Connell, T., Preece, J., Quesenbery, W., Shneiderman, B.: Usability and accessibility in consumer health informatics. American Journal of Preventive Medicine 40(2), 1 (2011)
Huang, D.S., Zhao, X.M., Huang, G.B., Cheung, Y.M.: Classifying protein sequences using hydropathy blocks. Pattern Recognition 39(12), 2293–2300 (2006)
Kuznetsov, V., Lee, H.K., Maurer-Stroh, S., Molnár, M.J., Pongor, S., Eisenhaber, B., Eisenhaber, F.: How bioinformatics influences health informatics: usage of biomolecular sequences, expression profiles and automated microscopic image analyses for clinical needs and public health. Health Information Science and Systems 1(2) (2013)
Langmead, B., Schatz, M.C., Lin, J., Pop, M., Salzberg, S.L.: Searching for snps with cloud computing. Genome Biol. 10(11), R134 (2009)
Liu, Y., Li, M., Cheung, Y.M., Sham, P.C., Ng, M.K.: Skm-snp: Snp markers detection method. Journal of Biomedical Informatics 43(2), 233–239 (2010)
Lu, C.-Y., Min, H., Zhao, Z.-Q., Zhu, L., Huang, D.-S., Yan, S.: Robust and efficient subspace segmentation via least squares regression. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 347–360. Springer, Heidelberg (2012)
Marru, S., Chae, H., Tangchaisin, P., Kim, S., Pierce, M., Nephew, K.: Transitioning biovlab cloud workbench to a science gateway. In: Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery (2011)
Perkins, S., Theiler, J.: Online feature selection using grafting. In: ICML, pp. 592–599 (2003)
Schatz, M.C.: Cloudburst: highly sensitive read mapping with mapreduce. Bioinformatics 25(11), 1363–1369 (2009)
Wang, J., Zhao, Z., Hu, X., Cheung, Y., Wang, M., Wu, X.: Online group feature selection. In: IJCAI (2013)
Wang, M., Gao, Y., Lu, K., Rui, Y.: View-based discriminative probabilistic modeling for 3d object retrieval and recognition. IEEE Transactions on Image Processing 22(4), 1395–1407 (2013)
Wu, X., Yu, K., Ding, W., Wang, H.: Online feature selection with streaming features. TPAMI 35(5), 1175–1192 (2013)
Yang, H., Xu, Z., King, I., Lyu, M.R.: Online learning for group lasso. In: ICML, pp. 1191–1198
Zhao, P., Hoi, S.C., Jin, R.: Double updating online learning. Journal of Machine Learning Research 12, 1587–1615 (2011)
Zhao, Z., Lei, W., Huan, L.: Efficient spectral feature selection with minimum redundancy. In: AAAI (2010)
Zhao, Z.Q., Glotin, H., Xie, Z., Gao, J., Wu, X.: Cooperative sparse representation in two opposite directions for semi-supervised image annotation. IEEE Transactions on Image Processing 21(9), 4218–4231 (2012)
Zhao, Z., Liu, H., Wang, J., Chang, Y.: Biological relevance detection via network dynamic analysis. In: BICoB, pp. 44–49. Citeseer (2010)
Zhao, Z., Wang, L., Liu, H., Ye, J.: On similarity preserving feature selection (2011)
Zhou, J., Foster, D.P., Stine, R., Ungar, L.H.: Streamwise feature selection using alpha-investing. In: KDD, pp. 384–393 (2005)
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Wang, J., Zhao, ZQ., Hu, X., Cheung, Ym., Hu, H., Gu, F. (2013). Online Learning towards Big Data Analysis in Health Informatics. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_52
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DOI: https://doi.org/10.1007/978-3-319-02753-1_52
Publisher Name: Springer, Cham
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