Semisupervised Classification With Novel Graph Construction for High-Dimensional Data

Yu, Zhiwen; Ye, Fengxu; Yang, Kaixiang; Cao, Wenming; Chen, C. L. Philip; Cheng, Lianglun; You, Jane; Wong, Hau-San

doi:10.1109/tnnls.2020.3027526

Cited by 10 publications

(2 citation statements)

References 52 publications

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“…Our approach exploring the subtype in an online fashion is closely related to unsupervised clustering [23], [53]. Along this line of research, K-means is used as an unsupervised initialization for convolutional layers sequentially, following a bottom-up fashion [54].…”

Section: Related Workmentioning

confidence: 99%

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Liu

Xing

You

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Unsupervised domain adaptation (UDA) has been successfully applied to transfer knowledge from a labeled source domain to target domains without their labels. Recently introduced transferable prototypical networks (TPN) further addresses class-wise conditional alignment. In TPN, while the closeness of class centers between source and target domains is explicitly enforced in a latent space, the underlying finegrained subtype structure and the cross-domain within-class compactness have not been fully investigated. To counter this, we propose a new approach to adaptively perform a finegrained subtype-aware alignment to improve performance in the target domain without the subtype label in both domains. The insight of our approach is that the unlabeled subtypes in a class have the local proximity within a subtype, while exhibiting disparate characteristics, because of different conditional and label shifts. Specifically, we propose to simultaneously enforce subtype-wise compactness and class-wise separation, by utilizing intermediate pseudo-labels. In addition, we systematically investigate various scenarios with and without prior knowledge of subtype numbers, and propose to exploit the underlying subtype structure. Furthermore, a dynamic queue framework is developed to evolve the subtype cluster centroids steadily using an alternative processing scheme. Experimental results, carried out with multi-view congenital heart disease data and VisDA and DomainNet, show the effectiveness and validity of our subtypeaware UDA, compared with state-of-the-art UDA methods.

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Section: Related Workmentioning

confidence: 99%

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Liu

Xing

You

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

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“…The development of graph neural networks [37]- [39] has attracted a lot of attention to exploring graph-based solutions for recommender systems [40]- [42]. For example, Ying et al [43] develop a data-efficient graph convolutional network by combining random walks and graph convolutions to generate embeddings for items.…”

Section: Gcn-based Recommendationmentioning

confidence: 99%

Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

Guo

Zhang

Chen

et al. 2022

IEEE Trans. Knowl. Data Eng.

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Shared-account Cross-domain Sequential Recommendation (SCSR) is an emerging yet challenging task that simultaneously considers the shared-account and cross-domain characteristics in the sequential recommendation. Existing works on SCSR are mainly based on Recurrent Neural Network (RNN) and Graph Neural Network (GNN) but they ignore the fact that although multiple users share a single account, it is mainly occupied by one user at a time. This observation motivates us to learn a more accurate user-specific account representation by attentively focusing on its recent behaviors. Furthermore, though existing works endow lower weights to irrelevant interactions, they may still dilute the domain information and impede the cross-domain recommendation. To address the above issues, we propose a reinforcement learning-based solution, namely RL-ISN, which consists of a basic cross-domain recommender and a reinforcement learning-based domain filter. Specifically, to model the account representation in the shared-account scenario, the basic recommender first clusters users' mixed behaviors as latent users, and then leverages an attention model over them to conduct user identification. To reduce the impact of irrelevant domain information, we formulate the domain filter as a hierarchical reinforcement learning task, where a high-level task is utilized to decide whether to revise the whole transferred sequence or not, and if it does, a low-level task is further performed to determine whether to remove each interaction within it or not. To evaluate the performance of our solution, we conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our RL-ISN method compared with the state-of-the-art recommendation methods.

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Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

Zhong

Xie

et al. 2023

Lecture Notes in Computer Science

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Semisupervised Classification With Novel Graph Construction for High-Dimensional Data

Cited by 10 publications

References 52 publications

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Subtype-Aware Dynamic Unsupervised Domain Adaptation

Reinforcement Learning-enhanced Shared-account Cross-domain Sequential Recommendation

Proximal Symmetric Non-negative Latent Factor Analysis: A Novel Approach to Highly-Accurate Representation of Undirected Weighted Networks

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