Transformation invariant subspace clustering
Transformation invariant subspace clustering
复制标题
变换不变子空间聚类
DOI：
10.1016/j.patcog.2016.02.006
复制
发表时间：
2016
影响因子：
8
通讯作者：
Tan Tieniu
中科院分区：
文献类型：
--
作者：
Li Qi;Sun Zhenan;Lin Zhouchen;He Ran;Tan Tieniu
子空间聚类在许多计算机视觉应用中取得了巨大的成功。然而，大多数子空间聚类算法需要良好对齐的数据样本，这通常不是直接实现的。本文提出了一种变换不变子空间聚类框架，通过联合对齐数据样本和学习子空间表示。通过比对，变换后的数据样本变得高度相关，并且可以获得更好的亲和矩阵。联合问题可以归结为一系列的最小二乘回归问题，这可以有效地解决。通过对未对齐的真实的数据进行大量实验，验证了该方法的有效性，实验结果表明，该方法比现有的子空间聚类算法和变换不变聚类算法具有更高的聚类精度.
Subspace clustering has achieved great success in many computer vision applications. However, most subspace clustering algorithms require well aligned data samples, which is often not straightforward to achieve. This paper proposes a Transformation Invariant Subspace Clustering framework by jointly aligning data samples and learning subspace representation. By alignment, the transformed data samples become highly correlated and a better affinity matrix can be obtained. The joint problem can be reduced to a sequence of Least Squares Regression problems, which can be efficiently solved. We verify the effectiveness of the proposed method with extensive experiments on unaligned real data, demonstrating its higher clustering accuracy than the state-of-the-art subspace clustering and transformation invariant clustering algorithms.