Locally linear representation for image clustering
Locally linear representation for image clustering
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DOI：
10.1049/el.2014.0666
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发表时间：
2013-04
影响因子：
1.1
通讯作者：
Liangli Zhen;Zhang Yi;Xi Peng;Dezhong Peng
中科院分区：
文献类型：
--
作者：
Liangli Zhen;Zhang Yi;Xi Peng;Dezhong Peng
相似图的构造在谱聚类算法中起着至关重要的作用。有两种流行的方案来构建相似图，即基于成对距离的方案（PDS）和基于线性表示的方案（LRS）。值得注意的是，上述方案分别存在一些局限性和缺点。具体而言，PDS对噪声和离群值敏感，而LRS可能错误地选择子空间间点来表示目标点。这些缺点大大降低了SC算法的性能。为了克服这些问题，提出了一种新的方案来构建相似性图，其中不同数据点之间的相似性计算取决于它们的成对距离和线性表示关系。该方案被称为局部线性表示（LLR），它使用一组数据点对每个数据点进行编码，这些数据点不仅产生最小的重建误差，而且接近目标点，这使得它对噪声和离群点具有鲁棒性，并且在很大程度上避免了选择子空间间的点来表示目标点。
The construction of the similarity graph plays an essential role in a spectral clustering (SC) algorithm. There exist two popular schemes to construct a similarity graph, i.e. the pairwise distance-based scheme (PDS) and the linear representation-based scheme (LRS). It is notable that the above schemes suffered from some limitations and drawbacks, respectively. Specifically, the PDS is sensitive to noises and outliers, while the LRS may incorrectly select inter-subspaces points to represent the objective point. These drawbacks degrade the performance of the SC algorithms greatly. To overcome these problems, a novel scheme to construct the similarity graph is proposed, where the similarity computation among different data points depends on both their pairwise distances and the linear representation relationships. This proposed scheme, called locally linear representation (LLR), encodes each data point using a collection of data points that not only produce the minimal reconstruction error but also are close to the objective point, which makes it robust to noises and outliers, and avoids selecting inter-subspaces points to represent the objective point to a large extent.