Feature Concatenation Multi-view Subspace Clustering

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.

References in corpus (10)

• Robust Recovery of Subspace Structures by Low-Rank Representation
• Proceedings of the 29th International Conference on Machine Learning (ICML-12)
• Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
• A Survey on Multi-view Learning
• Multi-view Low-rank Sparse Subspace Clustering
• Discriminative Block-Diagonal Representation Learning for Image Recognition
• Iterative Views Agreement: An Iterative Low-Rank based Structured Optimization Method to Multi-View Spectral Clustering
• A Survey on Multi-View Clustering
• Analysis and Improvement of Low Rank Representation for Subspace segmentation
• Visual Understanding via Multi-Feature Shared Learning with Global Consistency

Cited by in corpus (10)

• Self-Supervised Discriminative Feature Learning for Deep Multi-View Clustering
• Constrained Bilinear Factorization Multi-view Subspace Clustering
• Generalized Label Enhancement with Sample Correlations
• Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection
• Multi-view Hierarchical Clustering
• Multi-view Subspace Clustering Networks with Local and Global Graph Information
• Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering
• Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering
• Multi-typed Objects Multi-view Multi-instance Multi-label Learning
• Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion