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Yangdong Ye

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10 papers

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EAAI Journal 2026 Journal Article

Hyperbolic Adversarial Variational Embedding for item recommendation

Zhongchuan Sun
Liming Chen
Youwei Wang
Mingming Zhang
Yunpeng Wu
Yangdong Ye

Variational autoencoders (VAEs) have shown great promise in recommender systems due to their advantage of handling implicit feedback. However, existing VAE-based methods still rely on (i) static, data-independent Gaussian priors that fail to reflect actual user–item interaction dynamics, and (ii) Euclidean embeddings that distort the power-law structure of interaction patterns. To address these limitations, we propose Hyperbolic Adversarial Variational Embedding (HAVE), a unified framework that combines adversarial variational inference, data-driven prior adaptation, and non-Euclidean representation learning. First, we introduce an adversarial variational inference paradigm that matches the encoder’s posterior to a richer, learnable target distribution, thereby enhancing flexibility and capturing nuanced interest semantics. Building on this, we design an Adaptively Variational Prior (AdaPrior) which fuses user activity patterns and item popularity trends into behavior-aware priors, mitigating posterior collapse. Finally, HAVE embeds user behaviors into hyperbolic space, exploiting its hierarchical structure to naturally encode both a small core of highly active users and a vast periphery of casual users without distortion. Extensive experiments on multiple real-world datasets demonstrate that HAVE not only outperforms state-of-the-art baselines in recommendation accuracy but also yields latent representations that faithfully preserve hierarchical interaction structures.

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AAAI Conference 2026 Conference Paper

Interest-driven Deep Multi-modal Clustering

Guoliang Zou
Tongji Chen
Sijia Li
Jin Qin
Yangdong Ye
Shizhe Hu

Deep multi-modal clustering fully learns semantically consistent and discriminative cluster representations between multiple modalities in an unlabeled manner. However, existing methods treat all samples equally, ignoring varying sample quality, which limits clustering performance. Inspired by the concept of interest in the recommendation system, we propose a novel interest-driven deep multi-modal clustering (IDMC) framework. It designs a new paradigm to quantify the importance of each sample base on the attention it receives from other samples, which called interest value. This value jointly captures the local geometric structure through the Euclidean distance in feature space and the consistency of pseudo-labels. Then, we design a novel adaptive Bayesian fusion mechanism to dynamically balance the prior features and self-supervisory signals to ensure confidence-based sample importance estimation. Furthermore, we introduce a median normalization constraint and a label consistency constraint to further refine the construction of the interest value. By embedding this interest-guided value into representation learning and cluster optimization, IDMC focuses on the samples with the most information and the most stable semantics, thereby enhancing the performance of multi-modal representation learning. Extensive experiments verify that IDMC is superior to existing state-of-the-art methods in multiple evaluation metrics.

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AAAI Conference 2025 Conference Paper

Multi-aspect Self-guided Deep Information Bottleneck for Multi-modal Clustering

Shizhe Hu
Jiahao Fan
Guoliang Zou
Yangdong Ye

Deep multi-modal clustering can extract useful information among modals, thus benefiting the final clustering and many related fields. However, existing multi-modal clustering methods have two major limitations. First, they often ignore different levels of guiding information from both the feature representations and cluster assignments, which thus are difficult in learning discriminative representations. Second, most methods fail to effectively eliminate redundant information between multi-modal data, negatively affecting clustering results. In this paper, we propose a novel multi-aspect self-guided deep information bottleneck (MSDIB) method for multi-modal clustering, which can effectively employ different aspects of guiding information for learning cluster-friendly information among modals. MSDIB mainly contains two parts: information compression and information preservation. In information compression, we extract from the private information of each modality to obtain the compact representation and meanwhile conduct mutual compression between them. In information preservation, the aim is to preserve the shared information among modals and the self-supervised information from the clustering results in each iteration. In the above process, there are mainly three aspects of self-guiding information, the modality-private information, the modality-shared information and the self-supervised pseudo label information. By minimizing the mutual information based objective function with a variational optimization method, we can fully extract useful discriminative information while eliminating the irrelevant parts. Extensive experimental results demonstrate that our method outperforms state-of-the-art multi-modal clustering methods, showcasing its superior performance and broad application prospects.

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AAAI Conference 2025 Conference Paper

Self-supervised Trusted Contrastive Multi-view Clustering with Uncertainty Refined

Shizhe Hu
Binyan Tian
Weibo Liu
Yangdong Ye

Multi-view clustering (MVC), especially contrastive MVC, has demonstrated promising potential in many fields and practical scenarios. However, existing contrastive MVC methods still ignore the reliability of clustering results and the impact of false negative pairs, which limits the application of methods in critical security areas. To solve the above challenges, we propose a Self-supervised Trusted Contrastive Multi-view Clustering with Uncertainty Refined (STCMC-UR) method, which integrates clustering results and uncertainty learning to guide the self-supervised contrastive learning (CL). First, the belief of a specific view is generated in the evidence generation module. Afterwards, the belief mass and uncertainty of each view are learned using the Dirichlet distribution and we fuse multiple views with the Dempster-Shafer theory to generate the final clustering result and the uncertainty of the view. Then, the view weight is further quantified to adjust the belief of each view. Different from existing methods, with the clustering result and uncertainty generated by the fusion, we design a feature-level uncertainty-refined self-supervised CL module, where the pseudo-label is selectively employed in each iteration to conduct more accurate CL. As a result, the modules are mutually beneficial, which is conducive to more effective feature learning and clustering structure discovery, and more accurate learning results are obtained. Extensive experiments on five datasets show that the proposed method has significant improvements in effectiveness compared with the latest methods.

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AAAI Conference 2024 Conference Paper

Live and Learn: Continual Action Clustering with Incremental Views

Xiaoqiang Yan
Yingtao Gan
Yiqiao Mao
Yangdong Ye
Hui Yu

Multi-view action clustering leverages the complementary information from different camera views to enhance the clustering performance. Although existing approaches have achieved significant progress, they assume all camera views are available in advance, which is impractical when the camera view is incremental over time. Besides, learning the invariant information among multiple camera views is still a challenging issue, especially in continual learning scenario. Aiming at these problems, we propose a novel continual action clustering (CAC) method, which is capable of learning action categories in a continual learning manner. To be specific, we first devise a category memory library, which captures and stores the learned categories from historical views. Then, as a new camera view arrives, we only need to maintain a consensus partition matrix, which can be updated by leveraging the incoming new camera view rather than keeping all of them. Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are optimized. The empirical experimental results on 6 realistic multi-view action collections demonstrate the excellent clustering performance and time/space efficiency of the CAC compared with 15 state-of-the-art baselines.

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IJCAI Conference 2024 Conference Paper

Self-supervised Weighted Information Bottleneck for Multi-view Clustering

Zhengzheng Lou
Chaoyang Zhang
Hang Xue
Yangdong Ye
Qinglei Zhou
Shizhe Hu

Multi-view clustering (MVC) is a long-standing topic in machine learning and data mining community, focusing on investigating and utilizing the relationships among views for final consistent data cluster structure discovery. Generally, weighted MVC is one of the popular methods working by learning and applying the view weight/importance on each view for fully exploring the complementary information across views. However, most existing weighted MVCs only consider the quality of each view, ignoring the vital role of pseudo label self-supervision information in weight learning. In this work, we propose a novel self-supervised weighted information bottleneck (SWIB) method for solving the multi-view clustering problem. It combines the weighted information from different views based on information bottleneck theory, and the view weight learning mechanism is newly designed by simultaneously taking into accounting both the quality of view-contained information and the self-supervised information on the data partition of each view. Experimental results on multi-view text, multi-feature image, multi-angle video, and multi-modal text-image dataset as well as large-scale datasets show the superiority of the SWIB method. To our knowledge, this is the first work incorporating the self-supervised learning into weighted multi-view clustering.

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EAAI Journal 2023 Journal Article

Self-supervised temporal autoencoder for egocentric action segmentation

Mingming Zhang
Dong Liu
Shizhe Hu
Xiaoqiang Yan
Zhongchuan Sun
Yangdong Ye

Given an egocentric video, action temporal segmentation aims to temporally segment the video into basic units, each depicting an action. As the camera is constantly moving, some important objects may disappear in some consecutive frames and cause an abrupt change in the visual content. Recently works fail to deal with this condition in the absence of manually annotating abundant frames. In this study, we propose a temporal-aware clustering method for egocentric action temporal segmentation: a self-supervised temporal autoencoder (SSTAE). Instead of directly learning visual features, the SSTAE is implemented by encoding the preceding target frame and predicting subsequent frames in the temporal relationship domain, which takes into account the local temporal consistency. Our proposed algorithm is guided by the reconstruction and predicted losses. Consequently, local temporal contexts are naturally integrated into the feature representation, and a clustering step is performed. Experiments on three egocentric datasets demonstrate the our proposed approach outperforms the state-of-the-art methods by clustering Accuracy(ACC) 7. 57%, Normalized Mutual Information(NMI) 8. 17%, Adjusted Rand Index(ARI) 8. 6%.

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AAAI Conference 2021 Conference Paper

Deep Mutual Information Maximin for Cross-Modal Clustering

Yiqiao Mao
Xiaoqiang Yan
Qiang Guo
Yangdong Ye

Cross-modal clustering (CMC) aims to enhance the clustering performance by exploring complementary information from multiple modalities. However, the performances of existing CMC algorithms are still unsatisfactory due to the conflict of heterogeneous modalities and the high-dimensional non-linear property of individual modality. In this paper, a novel deep mutual information maximin (DMIM) method for cross-modal clustering is proposed to maximally preserve the shared information of multiple modalities while eliminating the superfluous information of individual modalities in an end-to-end manner. Specifically, a multi-modal shared encoder is firstly built to align the latent feature distributions by sharing parameters across modalities. Then, DMIM formulates the complementarity of multi-modalities representations as a mutual information maximin objective function, in which the shared information of multiple modalities and the superfluous information of individual modalities are identified by mutual information maximization and minimization respectively. To solve the DMIM objective function, we propose a variational optimization method to ensure it converge to a local optimal solution. Moreover, an auxiliary overclustering mechanism is employed to optimize the clustering structure by introducing more detailed clustering classes. Extensive experimental results demonstrate the superiority of D- MIM method over the state-of-the-art cross-modal clustering methods on IAPR-TC12, ESP-Game, MIRFlickr and NUS- Wide datasets.

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IJCAI Conference 2016 Conference Paper

Unsupervised Human Action Categorization with Consensus Information Bottleneck Method

Xiaoqiang Yan
Yangdong Ye
Xueying Qiu

Recent researches have shown consensus clustering can enhance the accuracy of human action categorization models by combining multiple clusterings, which can be obtained from various types of local descriptors, such as HOG, HOF and MBH. However, consensus clustering yields final clustering without access to the underlying feature representations of the human action data, which always makes the final partition limited to the quality of existing basic clusterings. To solve this problem, we present a novel and effective Consensus Information Bottleneck (CIB) method for unsupervised human action categorization. CIB is capable of learning action categories from feature variable and auxiliary clusterings simultaneously. Specifically, by performing Maximization of Mutual Information (MMI), CIB maximally preserves the information between feature variable and existing auxiliary clusterings. Moreover, to solve MMI optimization, a sequential solution is proposed to update data partition. Extensive experiments on five realistic human action data sets show that CIB can consistently and significantly beat other state-of-the-art consensus and multi-view clustering methods.

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IJCAI Conference 2013 Conference Paper

The Multi-Feature Information Bottleneck with Application to Unsupervised Image Categorization

Zhengzheng Lou
Yangdong Ye
Xiaoqiang Yan

We present a novel unsupervised data analysis method, Multi-feature Information Bottleneck (MfIB), which is an extension of the Information Bottleneck (IB). In comparison with the original IB, the proposed MfIB method can analyze the data simultaneously from multiple feature variables, which characterize the data from multiple cues. To verify the effectiveness of MfIB, we apply the corresponding MfIB algorithm to unsupervised image categorization. In our experiments, by taking into account multiple types of features, such as local shape, color and texture, the MfIB algorithm is found to be consistently superior to the original IB algorithm which takes only one source of features into consideration. Besides, the performance of MfIB algorithm is also superior to the state-ofthe-art unsupervised image categorization methods.

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