Scholarlink

<p>Low-rank representation (LRR) has been proved to be effective in exploring low-dimensional subspace structure embedded in the observations. However, existing LRR algorithms often pay no attention to data redundancy, easily leading to performance decay. In addition, the LRR characterizes data global inter-connections, from which some latent similarity features should be further learned and exploited to improve the performance of clustering. Therefore, a novel method termed Graph Regularized Independent Latent Low-Rank Representation (GRI-LLRR) is presented to address the above issues. As we know, Hilbert–Schmidt Independence Criterion (HSIC) measures the independence between two distributions. In the proposed method, it is introduced and developed to another novel graph regularization independent term to remove the uncorrelation between vectors and to preserve the data local geometry. With other constraints, including the sparse, nonnegative and symmetric, the LRR is obtained from the observations. Then, the proposed method further learns the cosine features as latent representation of the LRR for final clustering. Massive experiments have been conducted on eight benchmark data sets. Experimental results show that the proposed GRI-LLRR outperforms some state-of-the-art (SOTA) approaches with improvements of 2.24%, 2.73%, and 2.65% on average for CCA, NMI, and Purity, respectively.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Graph regularized independent latent low-rank representation for image clustering

  • 期刊论文

  • 发表日期：

    2025年2月12日

查看全文
  • Bo Li,
  • Lin-Feng Pan

摘要

Low-rank representation (LRR) has been proved to be effective in exploring low-dimensional subspace structure embedded in the observations. However, existing LRR algorithms often pay no attention to data redundancy, easily leading to performance decay. In addition, the LRR characterizes data global inter-connections, from which some latent similarity features should be further learned and exploited to improve the performance of clustering. Therefore, a novel method termed Graph Regularized Independent Latent Low-Rank Representation (GRI-LLRR) is presented to address the above issues. As we know, Hilbert–Schmidt Independence Criterion (HSIC) measures the independence between two distributions. In the proposed method, it is introduced and developed to another novel graph regularization independent term to remove the uncorrelation between vectors and to preserve the data local geometry. With other constraints, including the sparse, nonnegative and symmetric, the LRR is obtained from the observations. Then, the proposed method further learns the cosine features as latent representation of the LRR for final clustering. Massive experiments have been conducted on eight benchmark data sets. Experimental results show that the proposed GRI-LLRR outperforms some state-of-the-art (SOTA) approaches with improvements of 2.24%, 2.73%, and 2.65% on average for CCA, NMI, and Purity, respectively.