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Discriminative sparse coding on multi-manifolds

机译：多流形上的区分稀疏编码

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摘要

Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach.
机译：稀疏编码已被广泛用作计算机视觉，医学成像和生物信息学等各种应用中的有效数据表示方法。但是，常规的稀疏编码算法及其多变正规化的变体（图形稀疏编码和Laplacian稀疏编码）会以无人监督的方式学习密码本和代码，而忽略了训练集中可用的类别信息。为了解决这个问题，我们提出了一种基于多流形的新的区分稀疏编码方法，该方法从数据特征空间和分类标签中学习区分类条件条件码本和稀疏码。首先，根据类别标签将整个训练集划分为多个流形。然后，我们将稀疏编码公式化为流形流形匹配问题，并学习基于类的码本和代码，以最大程度地提高不同类的流形余量。最后，我们提出了一种基于数据样本流形匹配的策略来对未标记的数据样本进行分类。基于超声图像的体细胞突变鉴定和乳腺肿瘤分类的实验结果证明了所提出的数据表示和分类方法的有效性。

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  • 来源
    《Knowledge-Based Systems》 |2013年第12期|199-206|共8页
  • 作者

    Jim Jing-Yan Wang; Halima Bensmail; Nan Yao; Xin Gao;

  • 作者单位

    Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia;

    Qatar Computing Research Institute, Doha 5825, Qatar;

    Department of Instrumentation Engineering, Shanghai jiao Tong University, Shanghai 200240, China,National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China;

    Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia,Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Data representation; Sparse coding; Multi-manifolds; Urge margins;

    机译：数据表示;稀疏编码;多歧管;敦促利润;

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