A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by le...A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion.To further quantify the abstract features that characterize the data,a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance.Finally,the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty,and better performance is obtained.Experiments show that compared with the local similarity algorithm,the average precision is improved by 3.34%.Compared with the single-layer dictionary learning algorithm,the one-error is improved by 1.00%,and the average precision is improved by 0.54%.Experiments also show that it has better performance on general datasets.展开更多
Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability.Several centrality measures have been proposed recently to evaluate the performance of nodes...Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability.Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation,showing that the interaction between nodes has an influence on the importance of nodes.In this paper,a novel method based on node’s distribution and global influence in complex networks is proposed.The nodes in the complex networks are classified according to the distance matrix,then the correlation coefficient between pairs of nodes is calculated.From the whole perspective in the network,the global similarity centrality(GSC)is proposed based on the relevance and the shortest distance between any two nodes.The efficiency,accuracy,and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes.Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.展开更多
基金supported by the National Key Research and Development Project(2021YFF0901701)。
文摘A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion.To further quantify the abstract features that characterize the data,a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance.Finally,the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty,and better performance is obtained.Experiments show that compared with the local similarity algorithm,the average precision is improved by 3.34%.Compared with the single-layer dictionary learning algorithm,the one-error is improved by 1.00%,and the average precision is improved by 0.54%.Experiments also show that it has better performance on general datasets.
基金the National Natural Science Foundation of China(Nos.11361033,62162040 and 11861045)。
文摘Finding the important nodes in complex networks by topological structure is of great significance to network invulnerability.Several centrality measures have been proposed recently to evaluate the performance of nodes based on their correlation,showing that the interaction between nodes has an influence on the importance of nodes.In this paper,a novel method based on node’s distribution and global influence in complex networks is proposed.The nodes in the complex networks are classified according to the distance matrix,then the correlation coefficient between pairs of nodes is calculated.From the whole perspective in the network,the global similarity centrality(GSC)is proposed based on the relevance and the shortest distance between any two nodes.The efficiency,accuracy,and monotonicity of the proposed method are analyzed in two artificial datasets and eight real datasets of different sizes.Experimental results show that the performance of GSC method outperforms those current state-of-the-art algorithms.
