Search Results for author: Hongjing Zhang

Found 6 papers, 3 papers with code

Deep Fair Discriminative Clustering

1 code implementation28 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.

Clustering Deep Clustering +2

Deep Descriptive Clustering

no code implementations24 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.

Clustering Descriptive +1

A Framework for Deep Constrained Clustering

1 code implementation7 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.

Constrained Clustering

Towards Fair Deep Anomaly Detection

no code implementations29 Dec 2020 Hongjing Zhang, Ian Davidson

Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science.

Fairness One-Class Classification

A Graph-Based Approach for Active Learning in Regression

no code implementations30 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.

Active Learning regression

A Framework for Deep Constrained Clustering -- Algorithms and Advances

1 code implementation29 Jan 2019 Hongjing Zhang, Sugato Basu, Ian Davidson

The area of constrained clustering has been extensively explored by researchers and used by practitioners.

Constrained Clustering

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