ID 原文 译文
40366 该算法首先利用截断核范数正则化低秩分解模型对图像矩阵低秩分解得到低秩部分和稀疏部分， The algorithm firstly uses the truncated nuclear norm regularization low-rank decomposition model to decompose the low-rank part and sparse part of the image matrix.
40367 其中低秩部分保留了图像的主要信息，稀疏部分主要包含高频噪声及部分物体轮廓信息； The low-rank part retains the main information of the image, and the sparse part mainly contains high-frequency noise and some object contour information.
40368 然后对图像低秩部分进行分块，依据图像块纹理复杂度对图像块进行分类； Then, the low-rank part of the image is divided into blocks, and the image blocks are classified according to the texture complexity of the image block.
40369 最后使用K奇异值分解(K-single value decomposition，K-SVD)字典学习算法，针对不同类别训练出多个不同大小的过完备字典。 Finally, a K-single value decomposition(K-SVD)dictionary learning algorithm is used to train multiple over-complete dictionaries of different sizes for different categories.
40370 仿真结果表明，本文所提算法能够对图像进行较好的稀疏表示，并在很好地保持图像块特征一致性的同时显著提升图像重构质量。 Simulation results show that the proposed algorithm can perform better sparse representation of the image, while significantly maintaining the consistency of image block features and significantly improving the quality of image reconstruction.
40371 针对不完备信息系统的数据聚类问题，将集对分析理论引入k-means聚类中， For the data clustering problem of incomplete information system, the set pair analysis theory is introduced into k-means clustering.
40372 同时为了更好地表示样本与类簇的关系，构建了一种面向不完备信息系统的集对k-means(Set pair k-means，SPKM)聚类算法。 At the same time, to better represent the relationship between the sample and the cluster, a set pair k-means(SPKM)clustering algorithm for incomplete information system is constructed.
40373 首先，基于集对理论提出了一种集对距离度量方法，并将该度量方法运用到k-means算法中，得到初步聚类结果； Firstly, a set pair distance measurement method is proposed according to set pair theory, and the measurement method is applied to the k-means algorithm to obtain the preliminary clustering results.
40374 随后，对于同时属于多个类的样本，将其分配到相应类的边界域， Then, for samples belonging to multiple clusters at the same time, the samples are assigned into the boundary region of the corresponding clusters.
40375 对于只属于一个类的样本，将其分配到相应类的正同域或边界域， And for samples belonging to only one cluster, it is assigned into the positive region or boundary region of the corresponding clusters.