Abstract
From the viewpoint of image processing, a spectral feature-based TLS (Tikhonov-regularized least-squares) ensemble algorithm is proposed for tumor classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of atoms of an overcomplete dictionary. Two types of dictionaries, spectral feature-based eigenassays and spectral feature-based metasamples, are proposed for the TLS model. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.
This work was supported by a grant from National Science Foundation of China (No. 60905023) and a grant from National Science Foundation of Anhui Province (No. 1308085MF85).
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References
Zheng, C.Z., Zhang, L., Ng, T.Y., Shiu, C.K., Huang, D.S.: Metasample-based sparse representation for tumor classification. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8(5) (2011)
Rigamonti, R., Brown, M., Lepetit, V.: Are sparse representations really relevant for image classification? In: 24th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1545–1552 (2011)
Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: International Conference on Computer Vision, pp. 471–478 (2011)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision 42(3), 145–175 (2001)
Sun, Z.L., Rajan, D., Chia, L.T.: Scene classification using multiple features in a two-stage probabilistic classification framework. Neurocomputing 73(16-18), 2971–2979 (2010)
Sun, Z.L., Lam, K.M.: Depth estimation of face images based on the constrained ICA model. IEEE Transactions on Information Forensics and Security 6(2), 360–370 (2011)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: A method for large-scale ℓ1-regularized least squares. IEEE Journal on Selected Topics in Signal Processing 1(4), 606–617 (2007)
Alon, U., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat’l Academy of Sciences USA 96, 6745–6750 (1999)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Shipp, M.A., et al.: Diffuse large B-cell Lymphoma outcome prediction by gene expression profiling and supervised machine learning. Nature Medicine 8(1), 68–74 (2002)
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Sun, ZL., Wang, H., Lau, WS., Seet, G., Wang, D., Lam, KM. (2013). A Novel Ensemble Algorithm for Tumor Classification. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_36
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DOI: https://doi.org/10.1007/978-3-642-39068-5_36
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