# Dual Representation Learning for One-Step Clustering of Multi-View Data

arXiv: https://arxiv.org/abs/2208.14450 · HTML: https://mlsanity.com/paper/2208.14450/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 8 of 8)

- [Joint Optimization Framework for Learning with Noisy Labels](https://mlsanity.com/paper/1803.11364/) (2018)
- [On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization](https://mlsanity.com/paper/1808.02941/) (2018)
- [Adversarial Multi-task Learning for Text Classification](https://mlsanity.com/paper/1704.05742/) (2017)
- [Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization](https://mlsanity.com/paper/1803.08680/) (2018)
- [Connections Between Nuclear Norm and Frobenius Norm Based Representations](https://mlsanity.com/paper/1502.07423/) (2015)
- [Multi-view Clustering with the Cooperation of Visible and Hidden Views](https://mlsanity.com/paper/1908.04766/) (2019)
- [Multi-view learning with privileged weighted twin support vector machine](https://mlsanity.com/paper/2201.11306/) (2022)
- [Multi-view Information-theoretic Co-clustering for Co-occurrence Data](https://mlsanity.com/paper/1905.10594/) (2019)

## Built on by (most cited first, 1 of 1)

- [DRIMV_TSK: An Interpretable Surgical Evaluation Model for Incomplete Multi-View Rectal Cancer Data](https://mlsanity.com/paper/2506.17552/) (2025)

## Abstract (arXiv)

Multi-view data are commonly encountered in data mining applications. Effective extraction of information from multi-view data requires specific design of clustering methods to cater for data with multiple views, which is non-trivial and challenging. In this paper, we propose a novel one-step multi-view clustering method by exploiting the dual representation of both the common and specific information of different views. The motivation originates from the rationale that multi-view data contain not only the consistent knowledge between views but also the unique knowledge of each view. Meanwhile, to make the representation learning more specific to the clustering task, a one-step learning framework is proposed to integrate representation learning and clustering partition as a whole. With this framework, the representation learning and clustering partition mutually benefit each other, which effectively improve the clustering performance. Results from extensive experiments conducted on benchmark multi-view datasets clearly demonstrate the superiority of the proposed method.