为了构造一个能够较好反映数据真实分布的图以提高分类性能，文中提出基于l1范数和k近邻叠加图的半监督分类算法。首先构造一个l1范数图，作为主图，然后构造一个k近邻图，作为辅图，最后将二者按一定比例叠加，得到l1范数和k近邻叠加( LNKNNS)图。实验中选择标记样本比例从5%到25%，将基于LNKNNS图的半监督分类算法在USPS数据库上对比其它图(指数权重图、k近邻图、低秩表示图和l1范数图)的算法。实验表明，文中算法的分类识别率更高，更适合基于图的半监督学习。%A framework is proposed to construct a graph revealing the intrinsic structure of the data and improve the classification accuracy. In this framework, a l1-norm graph is constructed as the main graph and ak nearest neighbor ( KNN ) graph is constructed as auxiliary graph. Then, the l1-norm and KNN superposition ( LNKNNS ) graph is achieved as the weighted sum of the l1-norm graph and the KNN graph. The classification performance of LNKNNS-graph is compared with that of other graphs on USPS database, such as exp-weighted graph, KNNgraph, low rank graph and l1-norm graph, and 5% to 25%of the labeled samples are selected in experiments. Experimental results show that the classification accuracy of LNKNNS-graph algorithm is higher than that of other algorithms and the proposed framework is suitable for graph-based semi-supervised learning.
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