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	<title>Metasample Archives &#8212; MATLAB Number ONE</title>
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		<title>Metasample Based Sparse Representation for Tumor Classification</title>
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		<pubDate>Mon, 07 May 2018 05:13:21 +0000</pubDate>
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					<description><![CDATA[<p>A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1 -norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR based method [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/metasample-based-sparse-representation-for-tumor-classification/">Metasample Based Sparse Representation for Tumor Classification</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Molecular Pattern Discovery based on Penalized Matrix Decomposition</title>
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					<comments>https://matlab1.com/shop/matlab-code/molecular-pattern-discovery-based-on-penalized-matrix-decomposition/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Thu, 19 Apr 2018 13:29:54 +0000</pubDate>
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					<description><![CDATA[<p>In this project, we proposed to use the penalized matrix decomposition (PMD) to extract metasamples from gene expression data. With the sparsity constrain on the decomposition factors, the extracted metasamples can well capture the intrinsic structures of the samples in the same class. Meanwhile, the PMD factors of each sample are good indicators of the class label of it. [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/molecular-pattern-discovery-based-on-penalized-matrix-decomposition/">Molecular Pattern Discovery based on Penalized Matrix Decomposition</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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