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Shuping Zhao

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4 papers

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IJCAI Conference 2025 Conference Paper

High-Confident Local Structure Guided Consensus Graph Learning For Incomplete Multi-view Clustering

Shuping Zhao
Lunke Fei
Qi Lai
Jie Wen
Jinrong Cui
Tingting Chai

Current existing clustering methods for handling incomplete multi-view data primarily concentrate on learning a common representation or graph from the available views, while overlooking the latent information contained in the missing views and the imbalance of information among different views. Furthermore, instances with weak discriminative features usually degrading the precision of consistent representation or graph across all views. To address these problems, in this paper, we propose a simple but efficient method, called high-confident local structure guided consensus graph learning for incomplete multi-view clustering (HLSCG_IMC). Specifically, this method can adaptively learn a strict block diagonal structure from the available samples using a block diagonal representation regularizer. Different from the existing methods using a simple pairwise affinity graph for structure construction, we consider the influence of instances located at the edge of two clusters on the construction of graph for each view. By harnessing the proposed high-confident strict block diagonal structures, the approach seeks to directly guide the learning of the robust consensus graph. A number of experiments have been conducted to verify the efficacy of our approach.

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EAAI Journal 2024 Journal Article

Mutual dimensionless improved bearing fault diagnosis based on Bp-increment broad learning system in computer vision

Chunlin Li
Qintai Hu
Shuping Zhao
Jigang Wu
Jianbin Xiong

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial for ensuring normal machinery operation. However, the nonlinear and non-stationary vibration signals generated by machinery in harsh environments pose significant challenges in distinguishing fault signals from normal ones. Although several fault diagnosis methods based on mutual dimensionless indicators (MDI) have been proposed, they often fail to achieve effective and accurate health monitoring. Hence, this paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model based on the combined synergistic of two modules, to address the existing challenges. Firstly, a new mutual dimensionless indicator (VMDI) with high sensitivity and low overlap is refactored. Secondly, leveraging the advantages of incremental learning algorithms, a novel Broad Learning System (BLS) model for quickly identifying different fault types is constructed. Finally, the proposed method is validated using multiple datasets and verified through a comparative analysis with a published method based on dimensionless indicators (DI). The results demonstrate the effectiveness of the proposed method in fault diagnosis.

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EAAI Journal 2023 Journal Article

Joint multi-type feature learning for multi-modality FKP recognition

Yeping Yang
Lunke Fei
Adel Homoud Alshehri
Shuping Zhao
Weijun Sun
Shaohua Teng

Multimodal biometric recognition has attracted intensive attention due to its significant performance improvement for personal authentication by exploiting multiple resources of data. However, most existing multimodal biometric recognition methods tend to fuse completely different biometric traits, making them hard to explore the complementary features of the multimodal data. In this paper, we propose a new multimodal biometric descriptor by jointly learning the multi-type collaborative features of multi-modality finger-knuckle-print (FKP) images. First, we form multi-type feature vectors to capture the texture and direction patterns of the multimodal FKP images. Then, we jointly learn the feature projection to map multi-type vectors into a compact FKP descriptor. Moreover, our method automatically selects the optimal weights for multi-type features during feature learning to make the learned feature codes discriminative. Lastly, we integrate the non-overlapping block-wise histograms of the learned binary codes as the final multimodal FKP feature descriptor. The experimental results conducted on the benchmark PolyU FKP database demonstrate the effectiveness of the proposed method for multimodal FKP recognition.

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AAAI Conference 2023 Conference Paper

Tensorized Incomplete Multi-View Clustering with Intrinsic Graph Completion

Shuping Zhao
Jie Wen
Lunke Fei
Bob Zhang

Most of the existing incomplete multi-view clustering (IMVC) methods focus on attaining a consensus representation from different views but ignore the important information hidden in the missing views and the latent intrinsic structures in each view. To tackle these issues, in this paper, a unified and novel framework, named tensorized incomplete multi-view clustering with intrinsic graph completion (TIMVC_IGC) is proposed. Firstly, owing to the effectiveness of the low-rank representation in revealing the inherent structure of the data, we exploit it to infer the missing instances and construct the complete graph for each view. Afterwards, inspired by the structural consistency, a between-view consistency constraint is imposed to guarantee the similarity of the graphs from different views. More importantly, the TIMVC_IGC simultaneously learns the low-rank structures of the different views and explores the correlations of the different graphs in a latent manifold sub-space using a low-rank tensor constraint, such that the intrinsic graphs of the different views can be obtained. Finally, a consensus representation for each sample is gained with a co-regularization term for final clustering. Experimental results on several real-world databases illustrates that the proposed method can outperform the other state-of-the-art related methods for incomplete multi-view clustering.

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