A Group Norm Regularized Factorization Model for Subspace Segmentation

• URL: http://arxiv.org/abs/2001.02568v2
• Date: Tue, 14 Jul 2020 09:13:40 GMT
• Title: A Group Norm Regularized Factorization Model for Subspace Segmentation
• Authors: Xishun Wang and Zhouwang Yang and Xingye Yue and Hui Wang
• Abstract summary: This paper proposes a group norm regularized factorization model (GNRFM) inspired by the LRR model for subspace segmentation. Specifically, we adopt group norm regularization to make the columns of the factor matrix sparse, thereby achieving a purpose of low rank. Compared with traditional models and algorithms, the proposed method is faster and more robust to noise, so the final clustering results are better.
• Score: 4.926716472066594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Subspace segmentation assumes that data comes from the union of different subspaces and the purpose of segmentation is to partition the data into the corresponding subspace. Low-rank representation (LRR) is a classic spectral-type method for solving subspace segmentation problems, that is, one first obtains an affinity matrix by solving a LRR model and then performs spectral clustering for segmentation. This paper proposes a group norm regularized factorization model (GNRFM) inspired by the LRR model for subspace segmentation and then designs an Accelerated Augmented Lagrangian Method (AALM) algorithm to solve this model. Specifically, we adopt group norm regularization to make the columns of the factor matrix sparse, thereby achieving a purpose of low rank, which means no Singular Value Decompositions (SVD) are required and the computational complexity of each step is greatly reduced. We obtain affinity matrices by using different LRR models and then performing cluster testing on different sets of synthetic noisy data and real data, respectively. Compared with traditional models and algorithms, the proposed method is faster and more robust to noise, so the final clustering results are better. Moreover, the numerical results show that our algorithm converges fast and only requires approximately ten iterations.

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