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A p-norm singular value decomposition method for robust tumor clustering | IEEE Conference Publication | IEEE Xplore

A p-norm singular value decomposition method for robust tumor clustering


Abstract:

Tumor clustering based on biomolecular data plays a very important role for cancer classifications discovery. To further improve the robustness, stability and accuracy of...Show More

Abstract:

Tumor clustering based on biomolecular data plays a very important role for cancer classifications discovery. To further improve the robustness, stability and accuracy of tumor clustering, we develop a novel dimension reduction method named p-norm singular value decomposition (PSVD) to seek a low-rank approximation matrix to the bimolecular data. To enhance the robustness to outliers, the Lp-norm is taken as the error function and the Schatten p-norm is used as the regularization function in our optimization model. To evaluate the performance of PSVD, Kmeans clustering method is then employed for tumor clustering based on the low-rank approximation matrix. The extensive experiments are performed on gene expression dataset and cancer genome dataset respectively. All experimental results demonstrate that the PSVD-based method outperforms many existing methods. Especially it is experimentally proved that the proposed method is efficient for processing higher dimensional data with good robustness and superior time performance.
Date of Conference: 15-18 December 2016
Date Added to IEEE Xplore: 19 January 2017
ISBN Information:
Conference Location: Shenzhen, China

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