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中文摘要：
为提高三维模型的识别速度以及检索准确率,提出一种基于语义邻域的数据降维方法.通过基于内容的三维模型检索过程中的相关反馈记录,构造一个三维模型的语义邻接图,采用其中任意两点的最短路径长度来近似代替两点在流形空间上的测地距离,再通过多维尺度分析（MDS）算法来构造数据点在低维欧氏空间中的内在表示.对Princeton ShapeBenchmark的实验表明,该方法在数据的低维嵌入中保留了数据之间的语义关系,在三维模型检索中取得了更好的检索效果.
英文摘要：
A data dimension reduction method based on semantic neighbor was developed to improve the recognition speed and retrieval rate of 3D model. The semantic neighbor graph of 3D model was constructed by using content-based feedback records in the process of 3D model retrieval, and the shortest path length between two arbitrary points was selected to replace that in real characteristic manifold space. The intrinsic representation for data was constructed by using multidimensional scaling algorithm in low-dimensional Euclidean space. The experiments on Princeton Shape Benchmark show that the proposed method can hold semantic relationship in low-dimensional embedding of manifold for 3D model data and achieves good performance in 3D model retrieval.
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