基于改进局部保持投影的作物生长特性优化方法

IF 1.2 Q2 MATHEMATICS, APPLIED
Jia Dongyao, Hu Po, Zou Shengxiong
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引用次数: 0

摘要

局部保持投影(Locality preserving projection, LPP)仅保留部分信息,未考虑样本的类别信息,导致特征提取的误分类。为了优化生长特征的提取,提出了一种改进的局部保持投影算法。首先,利用二维主成分分析(2DPCA)对样本数据进行初步降维,保留空间信息;然后,定义两个优化子图来描述不同类别数据之间的邻域关系。最后,利用改进的LPP算法获得特征参数集,提取样本的局部信息。实验表明,改进的LPP算法具有良好的适应性,该方法的SVM分类准确率最高可达96%以上。与其他方法相比,改进的LPP在多维数据分析和优化方面具有优越的优化性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization Method for Crop Growth Characteristics Based on Improved Locality Preserving Projection
Locality preserving projection (LPP) retains only partial information, and category information of samples is not considered, which causes misclassification of feature extraction. An improved locality preserving projection algorithm is proposed to optimize the extraction of growth characteristics. Firstly, preliminary dimensionality reduction of sample data is constructed by using two-dimensional principal component analysis (2DPCA) to retain the spatial information. Then, two optimized subgraphs are defined to describe the neighborhood relation between different categories of data. Finally, feature parameters set are obtained to extract local information of samples by improved LPP algorithm. The experiments show that the improved LPP algorithm has good adaptability, and the highest SVM classification accuracy rate of this method can reach more than 96%. Compared with other methods, the improved LPP has superior optimized performance in terms of multidimensional data analysis and optimization.
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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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