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Issue No.03 - May-June (2012 vol.9)
pp: 818-827
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.
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
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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