# Non-Linear Fusion for Self-Paced Multi-View Clustering

arXiv: https://arxiv.org/abs/2104.09255 · HTML: https://mlsanity.com/paper/2104.09255/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Deep Density-based Image Clustering](https://mlsanity.com/paper/1812.04287/) (2018)
- [Beyond Low-Rank Representations: Orthogonal Clustering Basis Reconstruction with Optimized Graph Structure for Multi-view Spectral Clustering](https://mlsanity.com/paper/1708.02288/) (2017)

## Built on by (most cited first, 9 of 9)

- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [Adaptively-weighted Integral Space for Fast Multiview Clustering](https://mlsanity.com/paper/2208.12808/) (2022)
- [Variational Graph Generator for Multi-View Graph Clustering](https://mlsanity.com/paper/2210.07011/) (2022)
- [Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering](https://mlsanity.com/paper/2410.22983/) (2024)
- [Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2505.04339/) (2025)
- [Self-Paced Deep Regression Forests with Consideration of Ranking Fairness](https://mlsanity.com/paper/2112.06455/) (2021)
- [Self-Paced Neutral Expression-Disentangled Learning for Facial Expression Recognition](https://mlsanity.com/paper/2303.11840/) (2023)
- [Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views](https://mlsanity.com/paper/2410.09484/) (2024)
- [Anchor Learning with Potential Cluster Constraints for Multi-view Clustering](https://mlsanity.com/paper/2412.16519/) (2024)

## Abstract (arXiv)

With the advance of the multi-media and multi-modal data, multi-view clustering (MVC) has drawn increasing attentions recently. In this field, one of the most crucial challenges is that the characteristics and qualities of different views usually vary extensively. Therefore, it is essential for MVC methods to find an effective approach that handles the diversity of multiple views appropriately. To this end, a series of MVC methods focusing on how to integrate the loss from each view have been proposed in the past few years. Among these methods, the mainstream idea is assigning weights to each view and then combining them linearly. In this paper, inspired by the effectiveness of non-linear combination in instance learning and the auto-weighted approaches, we propose Non-Linear Fusion for Self-Paced Multi-View Clustering (NSMVC), which is totally different from the the conventional linear-weighting algorithms. In NSMVC, we directly assign different exponents to different views according to their qualities. By this way, the negative impact from the corrupt views can be significantly reduced. Meanwhile, to address the non-convex issue of the MVC model, we further define a novel regularizer-free modality of Self-Paced Learning (SPL), which fits the proposed non-linear model perfectly. Experimental results on various real-world data sets demonstrate the effectiveness of the proposed method.