# Multi-view Subspace Clustering via Partition Fusion

arXiv: https://arxiv.org/abs/1912.01201 · HTML: https://mlsanity.com/paper/1912.01201/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 8 of 8)

- [GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering](https://mlsanity.com/paper/2305.06799/) (2023)
- [Pseudo-supervised Deep Subspace Clustering](https://mlsanity.com/paper/2104.03531/) (2021)
- [Seeking Commonness and Inconsistencies: A Jointly Smoothed Approach to Multi-view Subspace Clustering](https://mlsanity.com/paper/2203.08060/) (2022)
- [Tensor-based Multi-view Spectral Clustering via Shared Latent Space](https://mlsanity.com/paper/2207.11559/) (2022)
- [Dual Information Enhanced Multi-view Attributed Graph Clustering](https://mlsanity.com/paper/2211.14987/) (2022)
- [Hyper-Laplacian Regularized Concept Factorization in Low-rank Tensor Space for Multi-view Clustering](https://mlsanity.com/paper/2304.11435/) (2023)
- [Double Graphs Regularized Multi-view Subspace Clustering](https://mlsanity.com/paper/2209.15143/) (2022)
- [Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction](https://mlsanity.com/paper/2407.00105/) (2024)

## Abstract (arXiv)

Multi-view clustering is an important approach to analyze multi-view data in an unsupervised way. Among various methods, the multi-view subspace clustering approach has gained increasing attention due to its encouraging performance. Basically, it integrates multi-view information into graphs, which are then fed into spectral clustering algorithm for final result. However, its performance may degrade due to noises existing in each individual view or inconsistency between heterogeneous features. Orthogonal to current work, we propose to fuse multi-view information in a partition space, which enhances the robustness of Multi-view clustering. Specifically, we generate multiple partitions and integrate them to find the shared partition. The proposed model unifies graph learning, generation of basic partitions, and view weight learning. These three components co-evolve towards better quality outputs. We have conducted comprehensive experiments on benchmark datasets and our empirical results verify the effectiveness and robustness of our approach.