Low-rank Kernel Learning for Graph-based Clustering

Abstract

Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning in kernel space has shown impressive performance on a number of benchmark data sets. However, its performance is largely determined by the chosen kernel matrix. To address this issue, the previous multiple kernel learning algorithm has been applied to learn an optimal kernel from a group of predefined kernels. This approach might be sensitive to noise and limits the representation ability of the consensus kernel. In contrast to existing methods, we propose to learn a low-rank kernel matrix which exploits the similarity nature of the kernel matrix and seeks an optimal kernel from the neighborhood of candidate kernels. By formulating graph construction and kernel learning in a unified framework, the graph and consensus kernel can be iteratively enhanced by each other. Extensive experimental results validate the efficacy of the proposed method.

References in corpus (2)

• Robust Subspace Clustering via Smoothed Rank Approximation
• YoTube: Searching Action Proposal via Recurrent and Static Regression Networks

Cited by in corpus (9)

• Robust Graph Learning from Noisy Data
• Multiple Kernel Clustering with Dual Noise Minimization
• Similarity Learning via Kernel Preserving Embedding
• Beyond Linear Subspace Clustering: A Comparative Study of Nonlinear Manifold Clustering Algorithms
• Scaled Simplex Representation for Subspace Clustering
• Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
• Multi-graph Fusion for Multi-view Spectral Clustering
• Latent Multi-view Semi-Supervised Classification
• Manifold Adaptive Multiple Kernel K-Means for Clustering