# A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning

arXiv: https://arxiv.org/abs/2005.00976 · HTML: https://mlsanity.com/paper/2005.00976/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2020.

## Built on by (most cited first, 19 of 19)

- [Multimodal Fusion on Low-quality Data: A Comprehensive Survey](https://mlsanity.com/paper/2404.18947/) (2024)
- [Incomplete Multi-View Multi-Label Learning via Label-Guided Masked View- and Category-Aware Transformers](https://mlsanity.com/paper/2303.07180/) (2023)
- [Is Parameter Collision Hindering Continual Learning in LLMs?](https://mlsanity.com/paper/2410.10179/) (2024)
- [DICNet: Deep Instance-Level Contrastive Network for Double Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2303.08358/) (2023)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (2023)
- [Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning](https://mlsanity.com/paper/2404.17340/) (2024)
- [Uncertainty-Aware Global-View Reconstruction for Multi-View Multi-Label Feature Selection](https://mlsanity.com/paper/2503.14024/) (2025)
- [Label Structure Preserving Contrastive Embedding for Multi-Label Learning with Missing Labels](https://mlsanity.com/paper/2209.01314/) (2022)
- [Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels](https://mlsanity.com/paper/2201.01079/) (2022)
- [A Robust Multilabel Method Integrating Rule-based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance](https://mlsanity.com/paper/2301.03283/) (2023)
- [Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification](https://mlsanity.com/paper/2409.07931/) (2024)
- [Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2501.06524/) (2025)
- [Adaptive Disentangled Representation Learning for Incomplete Multi-View Multi-Label Classification](https://mlsanity.com/paper/2601.05785/) (2026)
- [Pushing One Pair of Labels Apart Each Time in Multi-Label Learning: From Single Positive to Full Labels](https://mlsanity.com/paper/2302.14695/) (2023)
- [A Survey on Incomplete Multi-label Learning: Recent Advances and Future Trends](https://mlsanity.com/paper/2406.06119/) (2024)
- [Incomplete Multi-view Multi-label Classification via a Dual-level Contrastive Learning Framework](https://mlsanity.com/paper/2411.18267/) (2024)
- [Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets](https://mlsanity.com/paper/2506.09745/) (2025)
- [Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation](https://mlsanity.com/paper/2604.04170/) (2026)
- [When Semantically Consistent Encoding Meets View-Label Heterogeneity Modeling: A Unified Framework for Incomplete Multi-View Multi-Label Learning](https://mlsanity.com/paper/2609.07525/) (2026)

## Abstract (arXiv)

In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them, making even state-of-the-arts involve at least two explicit hyper-parameters such that model selection is quite difficult. More roughly, they will fail in handling the third challenge, let alone addressing the three jointly. In this paper, we aim at meeting these under the least assumption by building a concise yet effective model with just one hyper-parameter. To ease insufficiency of available labels, we exploit not only the consensus of multiple views but also the global and local structures hidden among multiple labels. Specifically, we introduce an indicator matrix to tackle the first two challenges in a regression form while aligning the same individual labels and all labels of different views in a common label space to battle the third challenge. In aligning, we characterize the global and local structures of multiple labels to be high-rank and low-rank, respectively. Subsequently, an efficient algorithm with linear time complexity in the number of samples is established. Finally, even without view-alignment, our method substantially outperforms state-of-the-arts with view-alignment on five real datasets.