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	<title>稀疏表示 Archives &#8212; MATLAB Number ONE</title>
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	<title>稀疏表示 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>
					<comments>https://matlab1.com/shop/matlab-code/regularized-robust-coding-for-face-recogntion/#respond</comments>
		
		<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>Robust Kernel Representation with Statistical Local Features for Face Recognition</title>
		<link>https://matlab1.com/shop/matlab-code/robust-kernel-representation-with-statistical-local-features-for-face-recognition/</link>
					<comments>https://matlab1.com/shop/matlab-code/robust-kernel-representation-with-statistical-local-features-for-face-recognition/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Tue, 08 May 2018 05:12:19 +0000</pubDate>
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					<description><![CDATA[<p>In this project , we proposed a statistical local feature based robust kernel representation (SLF-RKR) model for face recognition. A robust representation model to image outliers (e.g., occlusion and real disguise) was built in the kernel space, and a multi-partition max pooling technology was proposed to enhance the invariance of local pattern feature to image misalignment and pose [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/robust-kernel-representation-with-statistical-local-features-for-face-recognition/">Robust Kernel Representation with Statistical Local Features for Face Recognition</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>
		<link>https://matlab1.com/shop/matlab-code/metasample-based-sparse-representation-for-tumor-classification/</link>
					<comments>https://matlab1.com/shop/matlab-code/metasample-based-sparse-representation-for-tumor-classification/#respond</comments>
		
		<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>Metaface learning for sparse representation based face recognition</title>
		<link>https://matlab1.com/shop/matlab-code/metaface-learning-for-sparse-representation-based-face-recognition/</link>
					<comments>https://matlab1.com/shop/matlab-code/metaface-learning-for-sparse-representation-based-face-recognition/#respond</comments>
		
		<dc:creator><![CDATA[global MATLAB]]></dc:creator>
		<pubDate>Thu, 19 Apr 2018 07:38:47 +0000</pubDate>
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					<description><![CDATA[<p>Automatic face recognition (FR) has been, and remains being, one of the most visible and challenging research topics in computer vision, machine learning and biometrics. Although the facial images have a high dimensionality, they usually lie on a lower dimensional subspaces or submanifolds. Therefore, subspace learning and manifold learning methods have been dominantly and successfully used in appearance based FR , which [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/metaface-learning-for-sparse-representation-based-face-recognition/">Metaface learning for sparse representation based face recognition</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>
		<link>https://matlab1.com/shop/matlab-code/on-the-dimensionality-reduction-for-sparse-representation-based-face-recognition/</link>
					<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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