计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (2): 403-412.DOI: 10.3778/j.issn.1673-9418.2008064
孙武1, 邓赵红1,2,3,+(), 娄琼丹1, 顾鑫4, 王士同1
收稿日期:
2020-08-20
修回日期:
2020-11-03
出版日期:
2022-02-01
发布日期:
2020-11-19
通讯作者:
+ E-mail: dengzhaohong@jiangnan.edu.cn作者简介:
孙武(1995—),男,江苏兴化人,硕士研究生,主要研究方向为可解释人工智能。基金资助:
SUN Wu1, DENG Zhaohong1,2,3,+(), LOU Qiongdan1, GU Xin4, WANG Shitong1
Received:
2020-08-20
Revised:
2020-11-03
Online:
2022-02-01
Published:
2020-11-19
About author:
SUN Wu, born in 1995, M.S. candidate. His research interest is interpretability artificial inte-lligence.Supported by:
摘要:
异构领域自适应是一种借助源域知识为语义相关但特征空间不同的目标域建模的技术。现有的异构领域自适应方法大多属于半监督方法,这些方法要求目标域中存在一部分已标记样本,然而这种数据集在很多异构领域自适应任务中是稀缺的。为了解决上述问题,提出了一种新的基于模糊规则学习的无监督异构领域自适应算法。一方面,该方法基于TSK模糊系统的规则学习分别对源域和目标域进行特征学习,通过学习两个特征变换矩阵将源域和目标域投影到一个公共特征子空间;另一方面,为了减少因特征变换所造成的信息损失,该算法采取了多种信息保持策略,并且最大化公共特征子空间中源域数据和目标域数据之间的相关性。通过在几个真实领域自适应数据集上进行实验,验证了所提算法相对于现有的异构领域自适应方法具有一定的优越性。
中图分类号:
孙武, 邓赵红, 娄琼丹, 顾鑫, 王士同. 基于模糊规则学习的无监督异构领域自适应[J]. 计算机科学与探索, 2022, 16(2): 403-412.
SUN Wu, DENG Zhaohong, LOU Qiongdan, GU Xin, WANG Shitong. Unsupervised Heterogeneous Domain Adaptation with Fuzzy Rule Learning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(2): 403-412.
数据集 | 样本数 | 维度(SURF/DeCAF) | 类别数 | |
---|---|---|---|---|
Office-Caltech | A(Amazon) | 958 | 800/4 096 | 10 |
D(DSLR) | 157 | 800/4 096 | ||
W(Webcam) | 295 | 800/4 096 | ||
C(Caltech) | 1 123 | 800/4 096 | ||
Wiki | Img | 500 | 128 | 5 |
Txt | 500 | 10 | ||
Reuters | English | 18 758 | 227 | 6 |
French | 18 758 | 246 | ||
German | 18 758 | 284 | ||
Italian | 18 758 | 209 | ||
Spanish | 18 758 | 162 |
表1 数据集的统计信息
Table 1 Statistical information of datasets
数据集 | 样本数 | 维度(SURF/DeCAF) | 类别数 | |
---|---|---|---|---|
Office-Caltech | A(Amazon) | 958 | 800/4 096 | 10 |
D(DSLR) | 157 | 800/4 096 | ||
W(Webcam) | 295 | 800/4 096 | ||
C(Caltech) | 1 123 | 800/4 096 | ||
Wiki | Img | 500 | 128 | 5 |
Txt | 500 | 10 | ||
Reuters | English | 18 758 | 227 | 6 |
French | 18 758 | 246 | ||
German | 18 758 | 284 | ||
Italian | 18 758 | 209 | ||
Spanish | 18 758 | 162 |
算法 | 参数设置 |
---|---|
LinearCCA | |
CTSVM | |
CDLS | |
FUHDA-noSP | |
FUHDA-noTP | |
FUHDA-noCCA | |
FUHDA | |
表2 算法的参数设置
Table 2 Parameter settings of algorithms
算法 | 参数设置 |
---|---|
LinearCCA | |
CTSVM | |
CDLS | |
FUHDA-noSP | |
FUHDA-noTP | |
FUHDA-noCCA | |
FUHDA | |
Transfer tasks | Linear CCA | CTSVM | CDLS | FUHDA-noCCA | FUHDA-noSP | FUHDA-noTP | FUHDA |
---|---|---|---|---|---|---|---|
A_S2D | 75.55 | 71.47 | 93.68 | 28.18 | 88.83 | 87.37 | 93.84 |
A_D2S | 66.14 | 63.32 | 65.21 | 18.48 | 48.33 | 59.29 | 68.27 |
C_S2D | 47.86 | 45.45 | 88.79 | 37.40 | 83.97 | 85.40 | 88.87 |
C_D2S | 39.57 | 42.78 | 53.96 | 19.32 | 31.61 | 45.86 | 52.00 |
D_S2D | 17.31 | 57.69 | 77.17 | 27.39 | 99.36 | 22.93 | 99.36 |
D_D2S | 26.92 | 57.69 | 60.63 | 25.48 | 45.22 | 39.49 | 85.99 |
W_S2D | 53.06 | 72.45 | 97.07 | 32.88 | 99.66 | 73.56 | 99.66 |
W_D2S | 41.84 | 56.12 | 74.48 | 32.88 | 54.24 | 64.41 | 85.76 |
Average | 46.03 | 58.37 | 76.37 | 27.75 | 68.90 | 59.79 | 84.22 |
表3 各算法在Office-Caltech数据集上的准确度
Table 3 Accuracy of algorithms on Office-Caltech dataset %
Transfer tasks | Linear CCA | CTSVM | CDLS | FUHDA-noCCA | FUHDA-noSP | FUHDA-noTP | FUHDA |
---|---|---|---|---|---|---|---|
A_S2D | 75.55 | 71.47 | 93.68 | 28.18 | 88.83 | 87.37 | 93.84 |
A_D2S | 66.14 | 63.32 | 65.21 | 18.48 | 48.33 | 59.29 | 68.27 |
C_S2D | 47.86 | 45.45 | 88.79 | 37.40 | 83.97 | 85.40 | 88.87 |
C_D2S | 39.57 | 42.78 | 53.96 | 19.32 | 31.61 | 45.86 | 52.00 |
D_S2D | 17.31 | 57.69 | 77.17 | 27.39 | 99.36 | 22.93 | 99.36 |
D_D2S | 26.92 | 57.69 | 60.63 | 25.48 | 45.22 | 39.49 | 85.99 |
W_S2D | 53.06 | 72.45 | 97.07 | 32.88 | 99.66 | 73.56 | 99.66 |
W_D2S | 41.84 | 56.12 | 74.48 | 32.88 | 54.24 | 64.41 | 85.76 |
Average | 46.03 | 58.37 | 76.37 | 27.75 | 68.90 | 59.79 | 84.22 |
Transfer tasks | Linear CCA | CTSVM | CDLS | FUHDA-noCCA | FUHDA-noSP | FUHDA-noTP | FUHDA |
---|---|---|---|---|---|---|---|
Txt2Img | 39.80 | 43.40 | 44.20 | 51.40 | 55.20 | 55.00 | 60.40 |
Img2Txt | 80.00 | 78.40 | 92.83 | 30.00 | 91.40 | 91.60 | 95.20 |
Average | 59.90 | 60.90 | 68.51 | 40.70 | 73.30 | 73.30 | 77.80 |
表4 各算法在Wiki数据集上的准确度
Table 4 Accuracy of algorithms on Wiki dataset %
Transfer tasks | Linear CCA | CTSVM | CDLS | FUHDA-noCCA | FUHDA-noSP | FUHDA-noTP | FUHDA |
---|---|---|---|---|---|---|---|
Txt2Img | 39.80 | 43.40 | 44.20 | 51.40 | 55.20 | 55.00 | 60.40 |
Img2Txt | 80.00 | 78.40 | 92.83 | 30.00 | 91.40 | 91.60 | 95.20 |
Average | 59.90 | 60.90 | 68.51 | 40.70 | 73.30 | 73.30 | 77.80 |
Transfer tasks | Linear CCA | CTSVM | CDLS | FUHDA-noCCA | FUHDA-noSP | FUHDA-noTP | FUHDA |
---|---|---|---|---|---|---|---|
EN2SP | 33.00 | 49.66 | 49.86 | 23.80 | 34.96 | 42.87 | 54.82 |
FR2SP | 33.57 | 33.05 | 50.67 | 27.20 | 46.16 | 46.21 | 53.60 |
GR2SP | 65.08 | 59.23 | 50.36 | 27.20 | 30.28 | 47.43 | 57.24 |
IT2SP | 19.30 | 21.37 | 50.83 | 23.09 | 32.16 | 36.35 | 62.29 |
Average | 37.74 | 40.83 | 50.43 | 25.32 | 35.89 | 43.22 | 56.99 |
表5 各算法在Reuters数据集上的准确度
Table 5 Accuracy of algorithms on Reuters dataset %
Transfer tasks | Linear CCA | CTSVM | CDLS | FUHDA-noCCA | FUHDA-noSP | FUHDA-noTP | FUHDA |
---|---|---|---|---|---|---|---|
EN2SP | 33.00 | 49.66 | 49.86 | 23.80 | 34.96 | 42.87 | 54.82 |
FR2SP | 33.57 | 33.05 | 50.67 | 27.20 | 46.16 | 46.21 | 53.60 |
GR2SP | 65.08 | 59.23 | 50.36 | 27.20 | 30.28 | 47.43 | 57.24 |
IT2SP | 19.30 | 21.37 | 50.83 | 23.09 | 32.16 | 36.35 | 62.29 |
Average | 37.74 | 40.83 | 50.43 | 25.32 | 35.89 | 43.22 | 56.99 |
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