Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport

Gautheron, Léo; Redko, Ievgen; Lartizien, Carole

doi:10.1007/978-3-030-10928-8_45

Cited by 17 publications

(12 citation statements)

References 23 publications

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“…But if the exact space has large dimensions or the desired space is continuous, it is almost impossible to determine Q. To overcome this challenge, the neural network has been used, which results in Deep-Q -Learning [14].…”

Section: The Proposed Methods In the Feature Selectionmentioning

confidence: 99%

A New Method in Feature Selection based on Deep Reinforcement Learning in Domain Adaptation

Naman

Ameen

2022

eijs

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In data mining and machine learning methods, it is traditionally assumed that training data, test data, and the data that will be processed in the future, should have the same feature space distribution. This is a condition that will not happen in the real world. In order to overcome this challenge, domain adaptation-based methods are used. One of the existing challenges in domain adaptation-based methods is to select the most efficient features so that they can also show the most efficiency in the destination database. In this paper, a new feature selection method based on deep reinforcement learning is proposed. In the proposed method, in order to select the best and most appropriate features, the essential policies in deep reinforcement learning are defined, and then the selection features are applied for training random forest, k-nearest neighborhood and support vector machine classifiers. The trained classifiers with the considered features are evaluated on the target database. The results are evaluated with the criteria of accuracy, sensitivity, positive and negative predictive rates in the classifiers. The achieved results show the superiority of the proposed method of feature selection when used in domain adaptation. By implementing the RF classifier on the VisDA-2018 database and the Syn2Real database, the classification accuracy in the feature selection of the proposed deep learning reinforcement has increased compared to the two-feature selection of Laplace monitoring and feature selection states. The classification sensitivity with the help of SVM classifier on the Syn2Real databases had the highest values in the feature selection state of the proposed deep learning reinforcement. The obtained number 100 is a positive predictive rate in the Syn2Real database with the help of SVM classifier and in the case of selecting the proposed feature, it indicates its superiority. The negative predictive rate in the Syn2Real database in the state of feature selection of the proposed deep reinforcement learning was 100%, which showed its superiority in comparison with 90.1% in the state of selecting the Laplace monitoring feature. Gmean in KNN classifier on the Syn2Real database has improved in the feature selection state of the proposed deep learning reinforcement in comparison to without feature selection state.

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Section: The Proposed Methods In the Feature Selectionmentioning

confidence: 99%

A New Method in Feature Selection based on Deep Reinforcement Learning in Domain Adaptation

Naman

Ameen

2022

eijs

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“…Feature selection with MMD (f-MMD) [37] picks up domain-invariant features of source and target domains, but ignores the label information of training data, thus cannot assure the discriminative ability of the selected features. In [38], optimal transport between domains is used to select domain-invariant features, and then a traditional classifier is conducted on the selected features. Instead, we propose to select features that can jointly reduce the domain difference and minimize the training loss on the labeled source data, thus can find discriminative features shared by source and target domains.…”

Section: Related Workmentioning

confidence: 99%

Informative Feature Selection for Domain Adaptation

Sun

Luo

et al. 2019

IEEE Access

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Domain adaptation aims at extracting knowledge from an auxiliary source domain to assist the learning task in a target domain. When the data distribution of the target domain is different from that of the source domain, the direct use of source data for building a classifier for the target learning task cannot achieve promising performance. In this work, we propose a novel unsupervised domain adaptation method called Feature Selection for Domain Adaptation (FSDA), in which we aim to select a set of informative features. The benefits are two-fold. The first is to reduce the mismatch between the data distributions in the source and target domains by selecting a set of informative features in which they share similar properties. The second is to remove noisy features in the source domain such that the learning performance can be enhanced. We formulate a new sparse learning model for structured multiple outputs, including a vector to select informative features that can be used to jointly minimize the domain discrepancy and eliminate noisy features, and a classifier to perform prediction on the selected features. We develop a cutting-plane algorithm to solve the resulting optimization problem. Extensive experiments on real-world data sets are tested to demonstrate the effectiveness of the proposed method compared with the other existing methods.INDEX TERMS Domain adaptation, feature selection, structured multi-output learning, transfer learning.

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“…To address this challenge, the main research on domain adaptation techniques focuses on how a machine learning model built in a source domain can be adapted in a different but related target domain, which is necessary to avoid reconstruction efforts. In the field of knowledge engineering, many beneficial and promising examples with domain adaptation have been found, including image classification, object recognition, natural language processing, and feature learning [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

Liu

2020

Shock and Vibration

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Deep learning techniques have been widely used to achieve promising results for fault diagnosis. In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation. This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network. Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain. Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability. Meanwhile, the extracted features can efficiently improve the cross-domain testing performance. Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.

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Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport

Cited by 17 publications

References 23 publications

A New Method in Feature Selection based on Deep Reinforcement Learning in Domain Adaptation

A New Method in Feature Selection based on Deep Reinforcement Learning in Domain Adaptation

Informative Feature Selection for Domain Adaptation

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

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