Graph steered discriminative projections based on collaborative representation for Image recognition

Graph steered discriminative projections based on collaborative representation for Image recognition
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基于图像识别协作表示的图引导判别投影

DOI:
10.1007/s11042-018-7117-8
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发表时间:
2019-02
影响因子:
3.6
通讯作者:
Zhang Na
Zhang Na
中科院分区:
计算机科学4区
文献类型:
--
作者:
Liu Li;Zhang Bin;Zhang Huaxiang;Zhang Na

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图像识别中常用的降维技术有:我们提出了一个图导向的降维方法称为歧视性投影基于协作表示(DPCR)的降维任务转化为一个图嵌入框架。DPCR利用协作表示来构造类内和类间图。为了提高维数约简的判别性能,DPCR在图的构建中引入了标签信息。该方法不仅避免了寻找合适邻域的困难,而且继承了流形学习方法的优点和协同表示技术的鲁棒性。在基准数据集上的实验证明了该方法的有效性。
Dimensionality reduction techniques are commonly used for image recognition. We propose a graph steered dimensionality reduction method called Discriminative Projections based on Collaborative Representation (DPCR) by transforming the dimensionality reduction task into a graph embedding framework. DPCR utilizes the collaborative representation to construct within-class and between-class graphs. To improve the discriminative performance of dimensionality reduction, DPCR introduces the label information into graph building. The novel method not only avoids the difficulty of finding proper neighborhood but also inherits the merits of manifold learning methods and the robustness of collaborative representation techniques. Experiments on benchmark datasets demonstrate its effectiveness.
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