Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Reliable Conflictive Multi-View Learning (2402.16897v2)

Published 24 Feb 2024 in cs.LG and cs.AI

Abstract: Multi-view learning aims to combine multiple features to achieve more comprehensive descriptions of data. Most previous works assume that multiple views are strictly aligned. However, real-world multi-view data may contain low-quality conflictive instances, which show conflictive information in different views. Previous methods for this problem mainly focus on eliminating the conflictive data instances by removing them or replacing conflictive views. Nevertheless, real-world applications usually require making decisions for conflictive instances rather than only eliminating them. To solve this, we point out a new Reliable Conflictive Multi-view Learning (RCML) problem, which requires the model to provide decision results and attached reliabilities for conflictive multi-view data. We develop an Evidential Conflictive Multi-view Learning (ECML) method for this problem. ECML first learns view-specific evidence, which could be termed as the amount of support to each category collected from data. Then, we can construct view-specific opinions consisting of decision results and reliability. In the multi-view fusion stage, we propose a conflictive opinion aggregation strategy and theoretically prove this strategy can exactly model the relation of multi-view common and view-specific reliabilities. Experiments performed on 6 datasets verify the effectiveness of ECML.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (41)
  1. Multimodal learning with graphs. Nature Machine Intelligence, 5(4): 340–350.
  2. MV-HAN: A Hybrid Attentive Networks Based Multi-View Learning Model for Large-Scale Contents Recommendation. In Proceedings of the IEEE/ACM International Conference on Automated Software Engineering.
  3. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In International Conference on Machine Learning, volume 48, 1050–1059.
  4. A survey of uncertainty in deep neural networks. Artificial Intelligence Review, 1–77.
  5. Uncertainty-Aware Multi-View Representation Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 35(9): 7545–7553.
  6. Trusted Multi-View Classification. In International Conference on Learning Representations.
  7. Trusted Multi-View Classification With Dynamic Evidential Fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2): 2551–2566.
  8. Fast Multi-View Outlier Detection via Deep Encoder. IEEE Transactions on Big Data, 8(4): 1047–1058.
  9. Multi-view Subspace Clustering on Topological Manifold. In Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K.; and Oh, A., eds., Advances in Neural Information Processing Systems, volume 35, 25883–25894.
  10. Partially View-aligned Clustering. In Advances in Neural Information Processing Systems, volume 33, 2892–2902.
  11. Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering. Proceedings of the AAAI Conference on Artificial Intelligence, 37(7): 7936–7943.
  12. Jøsang, A. 2016. Subjective logic, volume 3. Springer.
  13. Uncertainty estimation for multi-view data: The power of seeing the whole picture. Advances in Neural Information Processing Systems, 35: 6517–6530.
  14. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles. In Advances in Neural Information Processing Systems, volume 30.
  15. Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning. In Computer Vision – ECCV 2010, 566–579.
  16. Foundations and recent trends in multimodal machine learning: Principles, challenges, and open questions. arXiv preprint arXiv:2209.03430.
  17. Safe Multi-View Deep Classification. In Proceedings of the AAAI Conference on Artificial Intelligence, 8870–8878.
  18. Trusted Multi-View Deep Learning with Opinion Aggregation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7): 7585–7593.
  19. Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), volume 124, 1308–1317.
  20. Clustering-Based Anomaly Detection in Multi-View Data. In Proceedings of the ACM International Conference on Information & Knowledge Management, 1545–1548.
  21. Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 462–472.
  22. Deep Evidential Learning with Noisy Correspondence for Cross-Modal Retrieval. In Proceedings of the ACM International Conference on Multimedia, 4948–4956.
  23. Evidential Deep Learning to Quantify Classification Uncertainty. In Advances in Neural Information Processing Systems, volume 31.
  24. 4sdrug: Symptom-based set-to-set small and safe drug recommendation. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3970–3980.
  25. On Deep Multi-View Representation Learning. In Proceedings of the International Conference on International Conference on Machine Learning - Volume 37, 1083–1092.
  26. Adversarial correlated autoencoder for unsupervised multi-view representation learning. Knowledge-Based Systems, 168: 109–120.
  27. Deep Double Incomplete Multi-View Multi-Label Learning With Incomplete Labels and Missing Views. IEEE Transactions on Neural Networks and Learning Systems, 1–13.
  28. Highly Confident Local Structure Based Consensus Graph Learning for Incomplete Multi-View Clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 15712–15721.
  29. A survey on incomplete multiview clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(2): 1136–1149.
  30. Unpaired Multi-View Graph Clustering With Cross-View Structure Matching. IEEE Transactions on Neural Networks and Learning Systems, 1–15.
  31. Adversarial incomplete multi-view clustering. In IJCAI, volume 7, 3933–3939.
  32. Uncertainty-aware multiview deep learning for internet of things applications. IEEE Transactions on Industrial Informatics, 19(2): 1456–1466.
  33. Partially View-Aligned Representation Learning With Noise-Robust Contrastive Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1134–1143.
  34. Deep Partial Multi-View Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5): 2402–2415.
  35. Multi-View Clustering via Triplex Information Maximization. IEEE Transactions on Image Processing, 32: 4299–4313.
  36. Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering. In Proceedings of the AAAI Conference on Artificial Intelligence, 11262–11269.
  37. Provable Dynamic Fusion for Low-Quality Multimodal Data. arXiv preprint arXiv:2306.02050.
  38. Late fusion multiple kernel clustering with local kernel alignment maximization. IEEE Transactions on Multimedia.
  39. Adaptive View-Aligned and Feature Augmentation Network for Partially View-Aligned Clustering. In Advances in Knowledge Discovery and Data Mining, 223–235.
  40. Consensus Regularized Multi-View Outlier Detection. IEEE Transactions on Image Processing, 27(1): 236–248.
  41. Tensorized Incomplete Multi-View Clustering with Intrinsic Graph Completion. In Proceedings of the AAAI Conference on Artificial Intelligence, 11327–11335.
Citations (37)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now