Subspace clustering by Mixture of Gaussian Regression
Subspace clustering by Mixture of Gaussian Regression
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DOI：
10.1109/cvpr.2015.7298821
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Baohua Li;Ying Zhang;Zhouchen Lin;Huchuan Lu
中科院分区：
文献类型：
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作者：
Baohua Li;Ying Zhang;Zhouchen Lin;Huchuan Lu
子空间聚类是寻找最适合从高维空间提取的样本点的多子空间表示的问题。现有的聚类模型一般采用不同的范数来描述噪声，这相当于假设数据受到特定类型的噪声的破坏。然而，在实践中，噪声要复杂得多。因此，简单地使用某种规范来模拟噪声是不合适的。因此，通过将噪声建模为混合高斯(Mog)，我们提出了对子空间聚类的混合高斯回归(Mog回归)。MOG回归提供了一种对更广泛的噪声分布进行建模的有效方法。结果表明，所得到的亲和度矩阵能够更好地刻画实际应用中的数据结构。在多个数据集上的实验结果表明，MOG回归方法的性能明显优于现有的子空间聚类方法。
Subspace clustering is a problem of finding a multi-subspace representation that best fits sample points drawn from a high-dimensional space. The existing clustering models generally adopt different norms to describe noise, which is equivalent to assuming that the data are corrupted by specific types of noise. In practice, however, noise is much more complex. So it is inappropriate to simply use a certain norm to model noise. Therefore, we propose Mixture of Gaussian Regression (MoG Regression) for subspace clustering by modeling noise as a Mixture of Gaussians (MoG). The MoG Regression provides an effective way to model a much broader range of noise distributions. As a result, the obtained affinity matrix is better at characterizing the structure of data in real applications. Experimental results on multiple datasets demonstrate that MoG Regression significantly outperforms state-of-the-art subspace clustering methods.