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计算机系统应用:2019,28(1):279-283
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利用PCA进行深度学习图像特征提取后的降维研究
杨博雄1,2, 杨雨绮2
(1.三亚学院 信息与智能工程学院, 三亚 572022;2.北京师范大学研究生院 珠海分院, 珠海 519085)
Applying PCA to Dimensionality Reduction of Image Features Extracted By Deep Learning
YANG Bo-Xiong1,2, YANG Yu-Qi2
(1.School of Information & Intelligence Engineering, Sanya University, Sanya 572022, China;2.Zhuhai Branch, Graduate School of Beijing Normal University, Zhuhai 519085, China)
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投稿时间:2018-06-08    修订日期:2018-06-27
中文摘要: 深度学习是当前人工智能领域广泛使用的一种机器学习方法.深度学习对数据的高度依赖性使得数据需要处理的维度剧增,极大地影响了计算效率和数据分类性能.本文以数据降维为研究目标,对深度学习中的各种数据降维方法进行分析.在此基础上,以Caltech 101图像数据集为实验对象,采用VGG-16深度卷积神经网络进行图像的特征提取,以PCA主成分分析方法为例来实现高维图像特征数据的降维处理.在实验阶段,采用欧氏距离作为相似性度量来检验经过降维处理后的精度指标.实验证明:当提取VGG-16神经网络fc3层的4096维特征后,使用PCA法将数据维度降至64维,依然能够保持较高的特征信息.
Abstract:Deep learning is a kind of machine learning method widely used in the field of artificial intelligence. The high dependence of deep learning on data makes the dimension of the data needed to be processed, which greatly affects the computing efficiency and the performance of data classification. Taking data dimensionality as the research goal, the methods of dimensionality reduction in deep learning are analyzed in this paper. Then, taking Caltech 101 image dataset as experimental object, VGG-16 depth convolution neural network is used to extract image features, and PCA statistical method is taken as an example to achieve dimensionality reduction of high-dimensional image feature data. Euclidean distance is used as a similarity measure to test the accuracy index after dimensionality reduction at the testing stage. The experiments show that image can still maintain high feature information using the PCA method to reduce the data dimension to 64 dimensions after extracting the 4096 dimensional feature of the fc3 layer of the VGG-16 neural network.
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基金项目:三亚学院高层次人才科研启动项目
引用文本:
杨博雄,杨雨绮.利用PCA进行深度学习图像特征提取后的降维研究.计算机系统应用,2019,28(1):279-283
YANG Bo-Xiong,YANG Yu-Qi.Applying PCA to Dimensionality Reduction of Image Features Extracted By Deep Learning.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):279-283

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