# Feature Concatenation Multi-view Subspace Clustering

## TL;DR

This paper introduces FCMSC, a novel multi-view subspace clustering method that uses feature concatenation and graph regularization to improve clustering performance by leveraging consensus and complementary information across views.

## Contribution

The paper proposes a new multi-view clustering approach that combines feature concatenation with $l_{2,1}$-norm and graph regularization, enhancing robustness and effectiveness over existing methods.

## Key findings

- Outperforms several state-of-the-art multi-view clustering methods.
- Effectively handles sample-specific and cluster-specific corruptions.
- Demonstrates superior results on six real-world datasets.

## Abstract

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, $l_{2,1}$-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.