隐式低秩稀疏表示的多视角子空间聚类

Latent Low-Rank Sparse Multi-view Subspace Clustering

针对多视角子空间聚类问题,提出基于隐式低秩稀疏表示的多视角子空间聚类算法(LLSMSC).算法构建多个视角共享的隐式结构,挖掘多视角之间的互补性信息.通过对隐式子空间的表示施加低秩约束和稀疏约束,捕获数据的局部结构和稀疏结构,使聚类结果更准确.同时,使用基于增广拉格朗日乘子交替方向最小化算法高效求解优化问题.在6个不同数据集上的实验验证LLSMSC的有效性和优越性.

To solve the problem of multi-view clustering,a latent low-rank sparse multi-view subspace clustering(LLSMSC)algorithm is proposed.A latent space shared by all views is constructed to explore the complementary information of multi-view data.The global and local structure of multi-view data can be captured to attain promising clustering results by imposing low-rank constraint and sparse constraint on the implicit latent subspace representation simultaneously.An algorithm based on augmented Lagrangian multiplier with alternating direction minimization strategy is employed to solve the optimization problem.Experiments on six benchmark datasets verify the effectiveness and superiority of LLSMSC.