具有鲁棒性的正交约束多视图子空间聚类算法

Orthogonal Constrained Multi-view Subspace Clustering Algorithm with Robustness

通过直接处理原始数据的每个视图,多视图子空间聚类算法通常可以获得潜在的子空间表示矩阵.然而,这些方法往往低估了冗余数据的影响,因此在潜在子空间表示中准确捕捉精确的聚类结果具有挑战性.此外,用于产生聚类结果的K-means算法很容易忽略子空间内数据的局部结构,导致结果不稳定.针对上述问题,本文提出了一种多视图子空间方法来获取高质量的子空间表示.具体来说,首先通过特征分解方法获得鲁棒性表示.然后,为多个视图构建一个联合潜在子空间表示.接下来,使用谱旋转来获得聚类结果,并对划分矩阵采用正交约束来重构子空间,从而提高聚类性能.最后,使用迭代优化算法来解决相关的优化问题.本文在5个基准数据集上进行了实验,结果表明,与最近的多视图聚类算法相比,本文的算法更加有效.

By directly processing each view of original data,multi-view subspace clustering algorithms typically obtain potential subspace representation matrices.However,these methods often underestimate the influences of redundant data,making it challenging to accurately capture the accurate clustering results in the potential subspace representation.Furthermore,the K-means algorithm used to produce the clustering results easily neglects the local structure of the data within the subspaces,leading to unstable results.To address the aforementioned problems,this study proposes a multiview subspace method to acquire high-quality subspace representations.Specifically,the study initially gets a robust representation through a feature decomposition method.Then,it constructs a joint latent subspace representation for multiple views.Next,it uses spectral rotation to obtain clustering results and employs orthogonal constraints on the partition matrix to reconstruct the subspaces,thereby enhancing clustering performance.Finally,an iterative optimization algorithm is applied to solve relevant optimization problems.Experiments are conducted on five benchmark datasets,and the results demonstrate that the proposed algorithm is more effective than recent multi-view clustering algorithms.