Abstract: Abstract: Subspace segmentation is one of the fundamentals in computer vision and machine learning. Given the large number of categories in practical problems concerning the data set, it is of great significance to address the issue of large subspace number subspace segmentation. Although spectral clustering-based methods received more attention in the field of subspace segmentation, the subspace number in the past experiments was usually less than 10. The infinity norm minimization was a recently proposed method specially for large subspace number subspace segmentation. It could effectively address this problem by reducing the difference of the representation matrix, but there remained some limitations. For example, the computation speed was not fast enough, and there was no theoretical guarantee for the independent subspaces. Therefore, a method named fast convex infinity norm minimization was proposed. This method can not only reduce the difference of the representation matrix, but also provide the theoretical guarantee for the independent subspace and enhance the computation speed, which has been testified by a large number of experiments.

Key words: Keywords: subspace segmentation, spectral clustering-based methods, large subspace number; infinity norm, fast algorithm