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Gene Classification Using Parameter-Free Semi-Supervised Manifold Learning
May-June 2012 (vol. 9 no. 3)
pp. 818-827
Hong Huang, Key Lab. on Opto-Electron. Tech. & Syst., Chongqing Univ., Chongqing, China
Hailiang Feng, Key Lab. on Opto-Electron. Tech. & Syst., Chongqing Univ., Chongqing, China
A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.

[1] Q. Cheng, "A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 7, no. 4, pp. 636-646, Oct.-Dec. 2010.
[2] Y.P. Li, X.H. Hu, H.F. Lin, and Z.H. Yang, "A Framework for Semisupervised Feature Generation and Its Applications in Biomedical Literature Mining," IEEE/ACM Trans. Computational Biology and Bioinformatics, vol. 8, no. 2, pp. 294-307, Mar./Apr. 2011.
[3] J.G. Lee and C.S. Zhang, "Classification of Gene-Expression Gata: The Manifold-Based Metric Learning Way," Pattern Recognition, vol. 39, no. 12, pp. 2450-2463, 2006.
[4] L.X. Zhao and Z.Y. Zhang, "Supervised Locally Linear Embedding with Probability-Based Distance for Classification," Computers and Math. with Applications, vol. 57, no. 6, pp. 919-926, 2009.
[5] S.T. Roweis and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, no. 5500, pp. 2323-2326, 2000.
[6] J.B. Tenenbaum, V. de Silva, and J.C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
[7] M. Belkin and P. Niyogi, "Laplacian Eigenmaps for Dimensionality Reduction and Data Representation," Neural Computation, vol. 15, no. 6, pp. 1373-1396, 2003.
[8] Z.Y. Zhang and H.Y. Zha, "Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment," SIAM J. Scientific Computing, vol. 26, no. 1, pp. 313-338, 2005.
[9] X.F. He, S.C. Yan, Y. Hu, P. Niyogi, and H.J. Zhang, "Face Recognition Using Laplacianfaces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, Mar. 2010.
[10] X.F. He et al., "Neighborhood Preserving Embedding," Proc. IEEE 10th Int'l Conf. Computer Vision, pp. 1208-1213, 2005.
[11] S.C. Yan, D. Xu, B. Zhang, H.J. Zhang, Q. Yang, and S. Lin, "Graph Embedding and Extensions: A General Framework for Dimensionality Reduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, Jan. 2007.
[12] J.N. Hu, W.H. Deng, J. Guo, and W.R. Xu, "Learning a Locality Discriminating Projection for Classification," Knowledge-Based Systems, vol. 22, no. 8, pp. 562-568, 2009.
[13] X.J. Zhu and A.B. Goldberg, Introduction to Semi-Supervised Learning. Morgan and Claypool, 2009.
[14] H.Y. Zha and Z.Y. Zhang, "Spectral Properties of the Alignment Matrices in Manifold Learning," J. SIAM Rev., vol. 51, no. 3, pp. 545-566, 2009.
[15] M. Sugiyama, T. Id, S. Nakajima, and J. Sese, "Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction," Machine Learning, vol. 78, nos. 1/2, pp. 35-61, 2010.
[16] M. Sugiyama, "Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis," J. Machine Learning Research, vol. 8, pp. 1027-1061, 2007.
[17] P. Belhumeur, J. Hespanda, and D. Kiregeman, "Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
[18] L. Zelnik-Manor and P. Perona, "Self-Tuning Spectral Clustering," Advances in Neural Information Processing Systems 17, vol. 17, pp. 1601-1608, 2005.
[19] Z. Jin, J.Y. Yang, Z.S. Hu, and Z. Lou, "Face Recognition Based on the Uncorrelated Discriminant Tranformation," Pattern Recognition, vol. 34, no. 7, pp. 1405-1416, 2001.
[20] J. Lu, "Enhanced Locality Sensitive Discriminant Analysis for Image Recognition," Electronics Letters, vol. 46, no. 3, pp. 213-214, 2010.
[21] J. Khan, J.S. Wei, M. Ringner, L.H. Saal, M. Ladanyi, and F. Westermann, "Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks," Nature Medicine, vol. 7, no. 6, pp. 673-679, 2001.
[22] A. Statnikov, C.F. Aliferis, I. Tsamardinos, D. Hardin, and S. Levy, "A Comprehensive Evaluation of Multicategory Classification Methods for Microarray Gene Expression Cancer Diagnosis," Bioinformatics, vol. 21, no. 5, pp. 631-643, 2005.
[23] A. Statnikov, I. Tsamardinos, Y. Dosbayev, and C.F. Aliferis, "GEMS: A System for Automated Cancer Diagnosis and Biomarker Discovery from Microarray Gene Expression Data," Int'l J. Medical Informatics, vol. 74, nos. 7/8, pp. 491-503, 2005.

Index Terms:
tumours,brain,eigenvalues and eigenfunctions,genetics,learning (artificial intelligence),medical computing,neurophysiology,optimisation,brain tumor gene expression data sets,gene classification,parameter-free semisupervised manifold learning,parameter-free semisupervised local fisher discriminant analysis,low-dimensional space,tumor classification,optimization objective function,global structure,globally optimal solution,eigen decomposition,SRBCT,DLBCL,Manifolds,Gene expression,Feature extraction,Training,Bioinformatics,Computational biology,Optimization,uncorrelated constraint.,Gene expression data,dimensionality reduction,semi-supervised local Fisher discriminant analysis,parameter free
Hong Huang, Hailiang Feng, "Gene Classification Using Parameter-Free Semi-Supervised Manifold Learning," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 9, no. 3, pp. 818-827, May-June 2012, doi:10.1109/TCBB.2011.152
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