Published 24-09-2024
Keywords
- unsupervised learning,
- representation learning,
- clustering
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
Artificial Intelligence (AI) has experienced rapid advancements, reshaping healthcare, autonomous driving, and finance industries. A critical factor in this progress is the efficiency and performance of AI models, which can be significantly enhanced through innovative learning techniques. Unsupervised representation learning is one of the most promising methods, and it provides an alternative to traditional supervised learning. Unlike supervised learning, which relies on labelled data to train models, unsupervised learning enables AI systems to discover meaningful patterns in raw, unlabeled data automatically. This approach trains models to represent data in a structured way, allowing them to identify hidden features and relationships without human intervention. As a result, AI models trained through unsupervised representation learning can excel in tasks such as clustering, anomaly detection, and feature extraction, often outperforming traditional methods in terms of efficiency and accuracy. The ability to uncover complex structures in data has wide-ranging implications across various fields, such as improving diagnostic systems in healthcare or enhancing decision-making in finance. Despite its potential, unsupervised representation learning comes with its own challenges, including evaluating the quality of learned representations and ensuring that models generalize well across different datasets. However, the ongoing development of techniques like deep learning and generative models is gradually addressing these hurdles, opening up new possibilities for AI systems that require less labelled data and can adapt more effectively to diverse tasks. This approach holds great promise for the future of AI, offering a path toward more efficient, scalable, and robust models that push the boundaries of what AI can achieve. As AI continues to evolve, unsupervised representation learning stands at the forefront of building models that can better understand and interact with the world around them.
Downloads
References
- Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8), 1798-1828.
- Jean, N., Wang, S., Samar, A., Azzari, G., Lobell, D., & Ermon, S. (2019, July). Tile2vec: Unsupervised representation learning for spatially distributed data. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 3967-3974).
- Bengio, Y. (2012, June). Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML workshop on unsupervised and transfer learning (pp. 17-36). JMLR Workshop and Conference Proceedings.
- Zhan, X., Xie, J., Liu, Z., Ong, Y. S., & Loy, C. C. (2020). Online deep clustering for unsupervised representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6688-6697).
- Rao, D., Visin, F., Rusu, A., Pascanu, R., Teh, Y. W., & Hadsell, R. (2019). Continual unsupervised representation learning. Advances in neural information processing systems, 32.
- Zhong, G., Wang, L. N., Ling, X., & Dong, J. (2016). An overview on data representation learning: From traditional feature learning to recent deep learning. The Journal of Finance and Data Science, 2(4), 265-278.
- Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022). Self-supervised representation learning: Introduction, advances, and challenges. IEEE Signal Processing Magazine, 39(3), 42-62.
- Tschannen, M., Bachem, O., & Lucic, M. (2018). Recent advances in autoencoder-based representation learning. arXiv preprint arXiv:1812.05069.
- Hamilton, W. L., Ying, R., & Leskovec, J. (2017). Representation learning on graphs: Methods and applications. arXiv preprint arXiv:1709.05584.
- Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015, June). Unsupervised learning of video representations using lstms. In International conference on machine learning (pp. 843-852). PMLR.
- Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2011). Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM, 54(10), 95-103.
- Sun, F. Y., Hoffmann, J., Verma, V., & Tang, J. (2019). Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. arXiv preprint arXiv:1908.01000.
- Kolve, E., Mottaghi, R., Han, W., VanderBilt, E., Weihs, L., Herrasti, A., ... & Farhadi, A. (2017). Ai2-thor: An interactive 3d environment for visual ai. arXiv preprint arXiv:1712.05474.
- Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., ... & McConley, M. (2018, December). Automated vulnerability detection in source code using deep representation learning. In 2018 17th IEEE international conference on machine learning and applications (ICMLA) (pp. 757-762). IEEE.
- DeVries, T., & Taylor, G. W. (2017). Dataset augmentation in feature space. arXiv preprint arXiv:1702.05538.
- Thumburu, S. K. R. (2023). Mitigating Risk in EDI Projects: A Framework for Architects. Innovative Computer Sciences Journal, 9(1).
- Thumburu, S. K. R. (2023). AI-Driven EDI Mapping: A Proof of Concept. Innovative Engineering Sciences Journal, 3(1).
- Gade, K. R. (2022). Data Modeling for the Modern Enterprise: Navigating Complexity and Uncertainty. Innovative Engineering Sciences Journal, 2(1).
- Gade, K. R. (2022). Migrations: AWS Cloud Optimization Strategies to Reduce Costs and Improve Performance. MZ Computing Journal, 3(1).
- Katari, A. Case Studies of Data Mesh Adoption in Fintech: Lessons Learned-Present Case Studies of Financial Institutions.
- Komandla, V. Enhancing Product Development through Continuous Feedback Integration “Vineela Komandla”.
- Thumburu, S. K. R. (2022). AI-Powered EDI Migration Tools: A Review. Innovative Computer Sciences Journal, 8(1).
- Katari, A., Ankam, M., & Shankar, R. Data Versioning and Time Travel In Delta Lake for Financial Services: Use Cases and Implementation.
- Gade, K. R. (2021). Cost Optimization Strategies for Cloud Migrations. MZ Computing Journal, 2(2).
- Thumburu, S. K. R. (2021). A Framework for EDI Data Governance in Supply Chain Organizations. Innovative Computer Sciences Journal, 7(1).