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Renjie Lin

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2 papers

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AAAI Conference 2026 Conference Paper

DIN: Dual Impulse Network for Multi-view Representation Learning

Yilin Wu
Weihong Lin
Renjie Lin
Zihan Fang
Shide Du
Shiping Wang

Multi-view representation learning, which utilizes multiple channels to improve perceptual accuracy, is recognized for its effectiveness in the analysis of multi-view data. However, deploying these methods in real-world scenarios presents two primary challenges. 1) Lack of Variegation: Multi-view representation techniques commonly observe along a singular axis, i.e., the attribute axis; 2) Insufficient Relationship: Most multi-view models lack mechanisms for exploring potential relationships between attribute axis and channel axis. To mitigate these obstacles, we design a Dual Impulse Network framework for multi-view representation learning (DIN) to train a feature representation. In this framework, a strategy observed along the channel axis and attribute axis simultaneously is introduced, and two different representations are generated by two analogous impulse networks, which are capable of extracting information corresponding to different axes. Furthermore, we incorporate an integration network that analyzes the potential relationship between attribute axis and channel axis to generate two attention matrices. The final two feature representations derived from these attention matrices are aggregated to amplify the expression of internal information. Comprehensive experimental results support the efficacy and superiority of the proposed framework, demonstrating improvements in classification performance compared to state-of-the-art methods.

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AAAI Conference 2026 Conference Paper

Graph Meets Deep Unfolding: An Interpretable Mutual-benefit Multi-view Learning Network

Renjie Lin
Hongzhi He
Yilin Wu
Shide Du
Le Zhang

Significant efforts have been focused on enhancing the utilization of multiple node features and topological structures in multi-view graph learning through explicit model-driven and implicit deep learning-based methodologies. The former excels in embedding prior knowledge, thereby offering theoretical interpretability but is limited in application flexibility due to manual parameter selection. In contrast, the latter leverages automatic differentiation, providing greater flexibility but lacking theoretical interpretability due to their opaque nature. Motivated by these observations, we propose an interpretable deep unfolding network for mutual-benefit multi-view graph learning, aiming to combine the strengths of both approaches. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve a multi-view graph learning model with sparse and low-rank constraints. This solution is then integrated into deep unfolding networks to enhance interpretability. Furthermore, we convert optimization conditions into implicit losses and utilize automatic differentiation to update parameters, reducing the need for manual tuning and increasing flexibility. This integration optimizes multi-view learning for a graph representation that balances interpretability and flexibility. Empirical evaluations on six diverse datasets demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.

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