基于改进KICA的多维图像特征约简

IF 1.2 Q2 MATHEMATICS, APPLIED
Jia Dongyao, Ai Yanke, Zou Shengxiong
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引用次数: 1

摘要

国内外对冗余多特征和噪声降维的研究不足,效率和精度较低。提出了基于改进核独立分量分析的特征参数模型降维优化方法;通过KICA(核独立成分分析)算法获得独立基元,构建独立的群子空间,同时使用2DPCA (2D主成分分析)算法完成与数据相关的二阶,并在上述方法中进一步降维。同时,提出了基于Amari误差和平均关联度的优化效果评价方法。对比仿真实验表明,Amari误差小于6%,平均关联度稳定在97%以上,参数优化方法可以有效地降低多维特征参数的维数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reduction of Multidimensional Image Characteristics Based on Improved KICA
The domestic and overseas studies of redundant multifeatures and noise in dimension reduction are insufficient, and the efficiency and accuracy are low. Dimensionality reduction and optimization of characteristic parameter model based on improved kernel independent component analysis are proposed in this paper; the independent primitives are obtained by KICA (kernel independent component analysis) algorithm to construct an independent group subspace, while using 2DPCA (2D principal component analysis) algorithm to complete the second order related to data and further reduce the dimension in the above method. Meanwhile, the optimization effect evaluation method based on Amari error and average correlation degree is presented in this paper. Comparative simulation experiments show that the Amari error is less than 6%, the average correlation degree is stable at 97% or more, and the parameter optimization method can effectively reduce the dimension of multidimensional characteristic parameters.
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来源期刊
Journal of Applied Mathematics
Journal of Applied Mathematics MATHEMATICS, APPLIED-
CiteScore
2.70
自引率
0.00%
发文量
58
审稿时长
3.2 months
期刊介绍: Journal of Applied Mathematics is a refereed journal devoted to the publication of original research papers and review articles in all areas of applied, computational, and industrial mathematics.
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