Mining the structural knowledge of high-dimensional medical data using isomap |
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Authors: | S Weng Dr C Zhang Z Lin X Zhang |
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Institution: | (1) State Key Laboratory of Intelligent Technology Systems, Department of Automation, Tsinghua University, Beijing, China;(2) MOE Key Laboratory of Bioinformatics, Department of Automation, Tsinghua University, Beijing, China |
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Abstract: | The paper describes an application of a new, non-linear dimensionality reduction method, named Isomap, for mining the structural
knowledge from high-dimensional medical data. The algorithm was evaluated on two publicly available medical datasets: the
pathological dataset of breast cancer (241 malignant samples) and the gene expression dataset from the lung (186 tumours).
It was found by Isomap that the approximate intrinsic dimensionalities of these two datasets were as low as three. The spatial
structures of both datasets were presented in low-dimensional space. Isomap, as a general tool for dimensionality reduction
analysis, is helpful in revealing the nonlinear structural knowledge of high-dimensional medical data. |
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Keywords: | Isomap Non-linear dimensionality reduction Breast cancer Lung cancer |
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