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	<title>Разреженное представление Archives &#8212; MATLAB Number ONE</title>
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	<title>Разреженное представление Archives &#8212; MATLAB Number ONE</title>
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		<title>Regularized Robust Coding for Face Recogntion</title>
		<link>https://matlab1.com/shop/matlab-code/regularized-robust-coding-for-face-recogntion/</link>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 08 May 2018 05:25:07 +0000</pubDate>
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					<description><![CDATA[<p>This paper presented a novel robust regularized coding (RRC) model and an associated effective iteratively reweighted regularized robust coding (IR3 C) algorithm for robust face recognition (FR). One important advantage of RRC is its robustness to various types of outliers (e.g., occlusion, corruption, expression, etc.) by seeking for an approximate MAP (maximum a posterior estimation) solution of the coding [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/regularized-robust-coding-for-face-recogntion/">Regularized Robust Coding for Face Recogntion</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Metasample Based Sparse Representation for Tumor Classification</title>
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		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<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>On the Dimensionality Reduction for Sparse Representation based Face Recognition</title>
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					<comments>https://matlab1.com/shop/matlab-code/on-the-dimensionality-reduction-for-sparse-representation-based-face-recognition/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Mon, 16 Apr 2018 03:59:10 +0000</pubDate>
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					<description><![CDATA[<p>In this project, we discussed the dimensionality reduction (DR) of face images when using sparse representation based classifier (SRC) for classification. Our experiments on Extended Yale B, AR and ORL face databases demonstrated that the proposed DR algorithm has better performance than Eigenfaces and Randomfaces. It can achieve higher recognition rate under the same dimensionality [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/on-the-dimensionality-reduction-for-sparse-representation-based-face-recognition/">On the Dimensionality Reduction for Sparse Representation based Face Recognition</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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