# Enriched Robust Multi-View Kernel Subspace Clustering

arXiv: https://arxiv.org/abs/2205.10495 · HTML: https://mlsanity.com/paper/2205.10495/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 65 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 6 of 6)

- [Large-scale Multi-view Subspace Clustering in Linear Time](https://mlsanity.com/paper/1911.09290/) (2019)
- [Multi-view Low-rank Sparse Subspace Clustering](https://mlsanity.com/paper/1708.08732/) (2017)
- [Structured Sparse Subspace Clustering: A Joint Affinity Learning and Subspace Clustering Framework](https://mlsanity.com/paper/1610.05211/) (2016)
- [Feature Concatenation Multi-view Subspace Clustering](https://mlsanity.com/paper/1901.10657/) (2019)
- [Spherical Principal Component Analysis](https://mlsanity.com/paper/1903.06877/) (2019)
- [Subspace Clustering via Thresholding and Spectral Clustering](https://mlsanity.com/paper/1303.3716/) (2013)

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

- [On Regularized Sparse Logistic Regression](https://mlsanity.com/paper/2309.05925/) (2023)

## Abstract (arXiv)

Subspace clustering is to find underlying low-dimensional subspaces and cluster the data points correctly. In this paper, we propose a novel multi-view subspace clustering method. Most existing methods suffer from two critical issues. First, they usually adopt a two-stage framework and isolate the processes of affinity learning, multi-view information fusion and clustering. Second, they assume the data lies in a linear subspace which may fail in practice as most real-world datasets may have non-linearity structures. To address the above issues, in this paper we propose a novel Enriched Robust Multi-View Kernel Subspace Clustering framework where the consensus affinity matrix is learned from both multi-view data and spectral clustering. Due to the objective and constraints which is difficult to optimize, we propose an iterative optimization method which is easy to implement and can yield closed solution in each step. Extensive experiments have validated the superiority of our method over state-of-the-art clustering methods.