Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The signif...Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.展开更多
大规模多视图聚类旨在解决传统多视图聚类算法中计算速度慢、空间复杂度高,以致无法扩展到大规模数据的问题.其中,基于锚点的多视图聚类方法通过使用整体数据集合的锚点集构建后者对于前者的重构矩阵,利用重构矩阵进行聚类,有效地降低...大规模多视图聚类旨在解决传统多视图聚类算法中计算速度慢、空间复杂度高,以致无法扩展到大规模数据的问题.其中,基于锚点的多视图聚类方法通过使用整体数据集合的锚点集构建后者对于前者的重构矩阵,利用重构矩阵进行聚类,有效地降低了算法的时间和空间复杂度.然而,现有的方法忽视了锚点之间的差异,均等地看待所有锚点,导致聚类结果受到低质量锚点的限制.为定位更具有判别性的锚点,加强高质量锚点对聚类的影响,提出一种基于加权锚点的大规模多视图聚类算法(Multi-view clustering with weighted anchors,MVC-WA).通过引入自适应锚点加权机制,所提方法在统一框架下确定锚点的权重,进行锚图的构建.同时,为增加锚点的多样性,根据锚点之间的相似度进一步调整锚点的权重.在9个基准数据集上与现有最先进的大规模多视图聚类算法的对比实验结果验证了所提方法的高效性与有效性.展开更多
文摘Multi-view Subspace Clustering (MVSC) emerges as an advanced clustering method, designed to integrate diverse views to uncover a common subspace, enhancing the accuracy and robustness of clustering results. The significance of low-rank prior in MVSC is emphasized, highlighting its role in capturing the global data structure across views for improved performance. However, it faces challenges with outlier sensitivity due to its reliance on the Frobenius norm for error measurement. Addressing this, our paper proposes a Low-Rank Multi-view Subspace Clustering Based on Sparse Regularization (LMVSC- Sparse) approach. Sparse regularization helps in selecting the most relevant features or views for clustering while ignoring irrelevant or noisy ones. This leads to a more efficient and effective representation of the data, improving the clustering accuracy and robustness, especially in the presence of outliers or noisy data. By incorporating sparse regularization, LMVSC-Sparse can effectively handle outlier sensitivity, which is a common challenge in traditional MVSC methods relying solely on low-rank priors. Then Alternating Direction Method of Multipliers (ADMM) algorithm is employed to solve the proposed optimization problems. Our comprehensive experiments demonstrate the efficiency and effectiveness of LMVSC-Sparse, offering a robust alternative to traditional MVSC methods.
文摘大规模多视图聚类旨在解决传统多视图聚类算法中计算速度慢、空间复杂度高,以致无法扩展到大规模数据的问题.其中,基于锚点的多视图聚类方法通过使用整体数据集合的锚点集构建后者对于前者的重构矩阵,利用重构矩阵进行聚类,有效地降低了算法的时间和空间复杂度.然而,现有的方法忽视了锚点之间的差异,均等地看待所有锚点,导致聚类结果受到低质量锚点的限制.为定位更具有判别性的锚点,加强高质量锚点对聚类的影响,提出一种基于加权锚点的大规模多视图聚类算法(Multi-view clustering with weighted anchors,MVC-WA).通过引入自适应锚点加权机制,所提方法在统一框架下确定锚点的权重,进行锚图的构建.同时,为增加锚点的多样性,根据锚点之间的相似度进一步调整锚点的权重.在9个基准数据集上与现有最先进的大规模多视图聚类算法的对比实验结果验证了所提方法的高效性与有效性.
