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首页> 外文期刊>Journal of information and computational science >A Novel Graph Construction Method Based on Locality Sensitive Histogram for Locality Preserving Projections
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A Novel Graph Construction Method Based on Locality Sensitive Histogram for Locality Preserving Projections

机译：基于局部敏感直方图的局部保留投影图构造新方法

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This paper presents a novel construction method of neighbor graph based on locality sensitive histogram for Locality Preserving Projections (LPP), which is called LSH Graph Construction. Unlike the conventional construction method of neighbor graph that vectorizes the original data set to compute the k-nearest neighbor graph, the LSH graph construction method which we proposed is acting on 2D image matrixes directly. Firstly, we compute each sample's locality sensitive histogram, so that each sample can be easily divided into multiple overlapping regions. Then we construct the samples' neighbor graph basing on corresponding region-to-region matching. Our proposed LSH Graph Construction method have the following two advantage: it can preserve samples' intrinsic structural information and is insensitive to illumination and pose variation in some extent. We apply LSH Graph Construction into the famous dimensionality algorithm: LPP to develope a new algorithm called LSHG-LPP. Experiments on two well-known face databases (Yale and ORL face databases) demonstrate that the proposed method outperforms LPP.
机译：本文提出了一种基于局部敏感直方图的局部保留投影（LPP）邻居图构造方法，称为LSH图构造。与传统的邻居图构造方法矢量化原始数据集以计算k最近邻居图不同，我们提出的LSH图构造方法直接作用于2D图像矩阵。首先，我们计算每个样本的位置敏感直方图，以便将每个样本轻松划分为多个重叠区域。然后，基于相应的区域间匹配，构造样本的邻居图。我们提出的LSH图构建方法具有以下两个优点：它可以保留样本的固有结构信息，并且对光照和姿势变化不敏感。我们将LSH图构造应用于著名的维度算法LPP中，以开发一种称为LSHG-LPP的新算法。在两个著名的人脸数据库（Yale和ORL人脸数据库）上进行的实验表明，该方法优于LPP。

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