基于人工智能技术的新媒体交互设计可视化系统

IF 0.8 Q4 Computer Science
Binbin Zhang
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引用次数: 1

摘要

实验结果表明,该算法的平均累计贡献率为92.78%,而传统算法的平均累计贡献率为88.88%。相比之下,该算法的平均累计贡献率提高了3.9%。在分类准确率方面,该算法的平均分类准确率为94.99%,而传统算法的平均分类准确率为90.98%。相比之下,该算法的平均分类准确率提高了4.01%。在降维时间方面,该算法的平均降维时间为3.46s,而传统算法的平均降维时间为6.43s。相比之下,该算法的平均降维时间缩短了2.97秒。从数据中可以看出,改进后的PCA算法可以有效地提高可视化系统的分类准确率和累积贡献率,缩短降维时间,提高系统处理数据的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Media Interactive Design Visualization System Based on Artificial Intelligence Technology
The experimental results show that the average cumulative contribution rate of this algorithm was 92.78%, while that of the traditional algorithm was 88.88%. In contrast, the average cumulative contribution rate of this algorithm was improved by 3.9%. In terms of classification accuracy, the average classification accuracy of this algorithm was 94.99%, while the traditional algorithm was 90.98%. In contrast, the average classification accuracy of this algorithm was improved by 4.01%. In terms of dimension reduction time, the average dimension reduction time of this algorithm was 3.46s, while that of the traditional algorithm was 6.43s. In contrast, the average dimension reduction time of this algorithm was shortened by 2.97s. It can be seen from the data that the improved PCA algorithm can effectively improve the classification accuracy and cumulative contribution rate of the visualization system, shorten the dimension reduction time, and improve the system's ability to process data.
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12.50%
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