Semi-supervised Learning with Graph Convolutional Networks Based on Hypergraph

Neural Processing Letters - Graph convolutional networks (GCNs), which rely on graph structures to aggregate information of neighbors to output robust node embeddings, have been becoming a popular...     

Self-Adaptive Clustering of Dynamic Multi-Graph Learning

Neural Processing Letters - In the process of graph clustering, the quality requirements for the structure of data graph are very strict, which will directly affect the final clustering results....     

液压轴向柱塞泵空载排量的测定方法研究

采用具有A级测试精度的液压泵综合性能试验台,按液压轴向柱塞泵的型式试验要求,分别测试在不同进出口压差、转速下泵的出口流量。基于测试数据,分别采用零压力截取法、一步TOET法和两步TOET法计算泵的空载排量,并进行对比分析。结果表明：采用零压力截取法计算得到的泵的空载排量受转速的影响较大,特别是在低速测试工况下,与泵的公称排量存在较大差异;而采用TOET法计算得到泵的空载排量更接近其公称排量。超过被测泵额定压力或额定转速下测得的数据可能会导致较大的数据偏差。     

A Robust Cost-Sensitive Feature Selection Via Self-Paced Learning Regularization

Neural Processing Letters - Feature selection is a useful and important process, which has a widely use in high-dimensional data processing and artificial intelligence. Its goal is to select a...     

Nonlinear sparse feature selection algorithm via low matrix rank constraint

The characteristics of non-linear, low-rank, and feature redundancy often appear in high-dimensional data, which have great trouble for further research. Therefore, a low-rank unsupervised feature selection algorithm based on kernel function is proposed. Firstly, each feature is projected into the high-dimensional kernel space by the kernel function to solve the problem of linear inseparability in the low-dimensional space. At the same time, the self-expression form is introduced into the deviation term and the coefficient matrix is processed with low rank and sparsity. Finally, the sparse regularization factor of the coefficient vector of the kernel matrix is introduced to implement feature selection. In this algorithm, kernel matrix is used to solve linear inseparability, low rank constraints to consider the global information of the data, and self-representation form determines the importance of features. Experiments show that comparing with other algorithms, the classification after feature selection using this algorithm can achieve good results.

     

Hypergraph expressing low-rank feature selection algorithm

Dimensionality reduction has been attracted extensive attention in machine learning. It usually includes two types: feature selection and subspace learning. Previously, many researchers have demonstrated that the dimensionality reduction is meaningful for real applications. Unfortunately, a large mass of these works utilize the feature selection and subspace learning independently. This paper explores a novel supervised feature selection algorithm by considering the subspace learning. Specifically, this paper employs an ? _2,1?norm and an ? _2,p ?norm regularizers, respectively, to conduct sample denoising and feature selection via exploring the correlation structure of data. Then this paper uses two constraints (i.e. hypergraph and low-rank) to consider the local structure and the global structure among the data, respectively. Finally, this paper uses the optimizing framework to iteratively optimize each parameter while fixing the other parameter until the algorithm converges. A lot of experiments show that our new supervised feature selection method can get great results on the eighteen public data sets.     

PPIS-JOIN: A Novel Privacy-Preserving Image Similarity Join Method

Neural Processing Letters - Recently, massive multimedia data (especially images) is moved to the cloud environment for analysis and retrieval, which makes data security issue become particularly...     

流道阻塞后的流场模型的开发及应用

在大破口失水事故进程中 ,燃料包壳可能发生的破裂将导致流道部分阻塞 ,在事故分析中必须考虑由此产生的影响。用COBRA Ⅳ Ⅰ子通道程序详细分析了流道阻塞后的流场 ,改进了大破口失水事故分析软件包中燃料棒包壳温度分析程序FRAP T6 ,对恰希玛核电厂大破口失水事故作了分析