基于模糊规则学习的无监督异构领域自适应

doi:10.3778/j.issn.1673-9418.2008064

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (2): 403-412.DOI: 10.3778/j.issn.1673-9418.2008064

基于模糊规则学习的无监督异构领域自适应

孙武¹, 邓赵红¹^,²^,³^,⁺(), 娄琼丹¹, 顾鑫⁴, 王士同¹

1.江南大学人工智能与计算机学院,江苏无锡 214122
2.复旦大学计算神经科学与类脑智能教育部重点实验室,上海 200433
3.张江实验室,上海 200120
4.江苏北方湖光光电有限公司,江苏无锡 214000

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。
邓赵红（1981—）,男,安徽蒙城人,教授,博士生导师,CCF杰出会员,主要研究方向为可解释和不确定性人工智能及其应用。
娄琼丹（1995—）,女,江苏邳州人,博士研究生,主要研究方向为可解释和不确定性人工智能与模式识别。
顾鑫（1979—）,男,江苏张家港人,博士,高级工程师,主要研究方向为模式识别、人工智能、图像处理技术研究与应用。
王士同（1964—）,男,江苏扬州人,教授,博士生导师,主要研究方向为人工智能、模式识别等。
基金资助:
国家自然科学基金面上项目(61772239);上海市“脑与类脑智能基础转化应用研究”市级重大科技专项(2018SHZDZX01)

Unsupervised Heterogeneous Domain Adaptation with Fuzzy Rule Learning

SUN Wu¹, DENG Zhaohong¹^,²^,³^,⁺(), LOU Qiongdan¹, GU Xin⁴, WANG Shitong¹

1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
2. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai 200433, China
3. Zhangjiang Lab, Shanghai 200120, China
4. Jiangsu North Huguang Opto-Electronics Co., Ltd., Wuxi, Jiangsu 214000, China

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.
DENG Zhaohong, born in 1981, professor, Ph.D. supervisor, distinguished member of CCF. His research interests include interpretability and un-certainty artificial intelligence and its applications.
LOU Qiongdan, born in 1995, Ph.D. candidate. Her research interests include interpretability and uncertainty artificial intelligence and pattern recognition.
GU Xin, born in 1979, Ph.D., senior engineer.His research interests include pattern recognition, artificial intelligence, image processing technology and its application.
WANG Shitong, born in 1964, professor, Ph.D. supervisor. His research interests include artificial intelligence, pattern recognition, etc.
Supported by:
National Natural Science Foundation of China(61772239);Municipal Science and Technology Major Project of “Basic Transformation and Application Research of Brain and Brain-Like Intelligence” in Shanghai(2018SHZDZX01)

摘要/Abstract

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。

关键词: 模糊规则学习, TSK模糊系统, 信息保持, 异构领域自适应

Abstract:

Heterogeneous domain adaptation is a technique that uses the knowledge of source domain to model the target domain. The source domain and the target domain are semantically related, but their feature spaces are different. Among existing heterogeneous domain adaptive methods, most of them belong to semi-supervised methods, which require some labeled samples in the target domain. However, this kind of dataset is rare in many heter-ogeneous adaptive tasks. In order to solve the above problem, this paper proposes a new unsupervised heter-ogeneous domain adaptive algorithm based on fuzzy rule learning. On the one hand, by introducing the TSK fuzzy system, the proposed method learns two feature transformation matrices corresponding to the source domain and the target domain respectively. By learning two feature transformation matrices, the source domain and the target domain are projected into a common feature subspace. On the other, in order to reduce the information loss caused by feature transformation, the proposed algorithm adopts a variety of information preservation strategies and maximizes the correlation between the transformed source domain data and target domain data. Through experi-ments on domain adaptive datasets, the results show that the proposed algorithm has certain advantages over the existing heterogeneous domain adaptive methods.

Key words: fuzzy rule learning, TSK fuzzy system, information preserving, heterogeneous domain adaptation

中图分类号:

TP181

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.

图/表 9

图1 基于TSK-FS的特征变换

Fig.1 Feature transformation based on TSK-FS

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
Wiki	Txt	500	10	5
Reuters	English	18 758	227	6
	French	18 758	246
	German	18 758	284
	Italian	18 758	209
	Spanish	18 758	162

表2 算法的参数设置

Table 2 Parameter settings of algorithms

算法	参数设置
LinearCCA	$C = {2 - 15, 2 - 14, ⋯, 29}$
CTSVM	$C = {2 - 15, 2 - 14, ⋯, 29}$
CDLS	$T = 5$ , $δ = 0.5$
FUHDA-noSP	$K = 3$ , $T = 15$ , $α = {2 - 9, 2 - 8, ⋯, 29}$ , $μ = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$
FUHDA-noTP	$K = 3$ , $T = 15$ , $α = {2 - 9, 2 - 8, ⋯, 29}$ , $β = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$
FUHDA-noCCA	$K = 3$ , $T = 15$ , $β = {2 - 9, 2 - 8, ⋯, 29}$ , $μ = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$
FUHDA	$K = 3$ , $T = 15$ , $α = {2 - 9, 2 - 8, ⋯, 29}$ , $β = {2 - 9, 2 - 8, ⋯, 29}$ , $μ = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$

表2 算法的参数设置

Table 2 Parameter settings of algorithms

算法	参数设置
LinearCCA	$C = {2 - 15, 2 - 14, ⋯, 29}$
CTSVM	$C = {2 - 15, 2 - 14, ⋯, 29}$
CDLS	$T = 5$ , $δ = 0.5$
FUHDA-noSP	$K = 3$ , $T = 15$ , $α = {2 - 9, 2 - 8, ⋯, 29}$ , $μ = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$
FUHDA-noTP	$K = 3$ , $T = 15$ , $α = {2 - 9, 2 - 8, ⋯, 29}$ , $β = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$
FUHDA-noCCA	$K = 3$ , $T = 15$ , $β = {2 - 9, 2 - 8, ⋯, 29}$ , $μ = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$
FUHDA	$K = 3$ , $T = 15$ , $α = {2 - 9, 2 - 8, ⋯, 29}$ , $β = {2 - 9, 2 - 8, ⋯, 29}$ , $μ = {2 - 9, 2 - 8, ⋯, 29}$ , $λ = 1$

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

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

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

图2 规则数分析

Fig.2 Rule number analysis

图3 收敛性分析

Fig.3 Convergence analysis

图4 参数敏感性分析

Fig.4 Parameter sensitivity analysis

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基于模糊规则学习的无监督异构领域自适应

Unsupervised Heterogeneous Domain Adaptation with Fuzzy Rule Learning

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