1 code implementation • 28 May 2021 • Hongjing Zhang, Ian Davidson
Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as $k$-means and spectral clustering.
no code implementations • 24 May 2021 • Hongjing Zhang, Ian Davidson
However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable.
1 code implementation • 7 Jan 2021 • Hongjing Zhang, Tianyang Zhan, Sugato Basu, Ian Davidson
A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering.
no code implementations • 29 Dec 2020 • Hongjing Zhang, Ian Davidson
Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science.
no code implementations • 30 Jan 2020 • Hongjing Zhang, S. S. Ravi, Ian Davidson
Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query.
1 code implementation • 29 Jan 2019 • Hongjing Zhang, Sugato Basu, Ian Davidson
The area of constrained clustering has been extensively explored by researchers and used by practitioners.