# Feature Concatenation Multi-view Subspace Clustering

arXiv: https://arxiv.org/abs/1901.10657 · HTML: https://mlsanity.com/paper/1901.10657/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2019.

## Built on by (most cited first, 14 of 14)

- [Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering](https://mlsanity.com/paper/2106.11232/) (2021)
- [Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering](https://mlsanity.com/paper/2103.15069/) (2021)
- [Deep Multi-View Subspace Clustering with Anchor Graph](https://mlsanity.com/paper/2305.06939/) (2023)
- [Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles](https://mlsanity.com/paper/2408.06819/) (2024)
- [MCoCo: Multi-level Consistency Collaborative Multi-view Clustering](https://mlsanity.com/paper/2302.13339/) (2023)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion](https://mlsanity.com/paper/2108.08301/) (2021)
- [Semantically Consistent Multi-view Representation Learning](https://mlsanity.com/paper/2303.04366/) (2023)
- [Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection](https://mlsanity.com/paper/2210.04271/) (2022)
- [Attentive Multi-View Deep Subspace Clustering Net](https://mlsanity.com/paper/2112.12506/) (2021)
- [Enriched Robust Multi-View Kernel Subspace Clustering](https://mlsanity.com/paper/2205.10495/) (2022)
- [Preventing Model Collapse in Deep Canonical Correlation Analysis by Noise Regularization](https://mlsanity.com/paper/2411.00383/) (2024)
- [Efficient and Effective Deep Multi-view Subspace Clustering](https://mlsanity.com/paper/2310.09718/) (2023)
- [A Joint Sparse Self-Representation Learning Method for Multiview Clustering](https://mlsanity.com/paper/2508.06857/) (2025)

## Abstract (arXiv)

Multi-view clustering is a learning paradigm based on multi-view data. Since statistic properties of different views are diverse, even incompatible, few approaches implement multi-view clustering based on the concatenated features straightforward. However, feature concatenation is a natural way to combine multi-view data. To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data. Specifically, multi-view data are concatenated into a joint representation firstly, then, l2,1-norm is integrated into the objective function to deal with the sample-specific and cluster-specific corruptions of multiple views. Moreover, a graph regularized FCMSC is also proposed in this paper to explore both the consensus information and complementary information of multi-view data for clustering. It is noteworthy that the obtained coefficient matrix is not derived by simply applying the Low-Rank Representation (LRR) to concatenated features directly. Finally, an effective algorithm based on the Augmented Lagrangian Multiplier (ALM) is designed to optimize the objective functions. Comprehensive experiments on six real-world datasets illustrate the superiority of the proposed methods over several state-of-the-art approaches for multi-view clustering.