复杂细微差异化网络数据特征的语义优化提取算法

对网络数据的复杂、细微、差异化特征进行语义提取,是实现Web网络数据准确识别和检索的关键技术。复杂、细微、差异化的网络数据语义特征具有非线性和随机散布性的特点,其主题分布广、更新频率大,从而造成语义特征提取困难。传统方法采用小波基函数投影算法进行语义特征的提取,性能不好。提出了一种基于Dopplerlet变换匹配投影的网络数据特征语义优化提取算法。首先构建语义高斯边缘化矩形窗函数进行融合滤波处理,通过文本切分把大量的信息熵数据进行小波基函数投影,有效剔除簇内异常数据；然后利用Dopplerlet变换匹配投影的自相似特性,自适应匹配语义的非线性谱特征,在Hilbert张成子空间中,实现对语义特征的提取和优化表达,再完成提取。仿真实验表明,该算法提高了对网络数据特征语义的表达能力,能有效区分差异网络数据中的冗余数据和残差数据,提高对杂细微差异化网络数据的检测识别和检索能力。

Optimized Semantic Extraction Algorithm of Complex Subtle Differentiated Network Data Characteristics

The complex subtle differentiated network data need to be extracted for semantic,and it is a key technology to realize the accurate identification and retrieval of Web network data.The network data have nonlinear and random distribution,subject distribution is wide,and the update frequency is fast,so it is difficult to extract.A semantic optimization feature extraction algorithm for network data was proposed based on the Dopplerlet transform projection.The semantic Gauss edge rectangle window function is given,and data fusion system of difference fusion filtering is constructed.The text segmentation is taken,and massive data of information entropy are constructed,which can effectively elimi-nate the abnormal data in the cluster.Self similar characteristics of Dopplerlet transform are used for matching the projection.Nonlinear adaptive matching semantic spectrum feature is extracted,and maximal linearly independent group is searched out at the Hilbert subspace.Simulation results show that the algorithm can increase the feature semantic expression ability,effectively distinguish the differences between redundant data and residual data in network data,and improve the subtle error detection and retrieval capabilities for heterozygous network.