Fixed-rank representation for unsupervised visual learning

Subspace clustering and feature extraction are two of the most commonly used unsupervised learning techniques in computer vision and pattern recognition. State-of-theart techniques for subspace clustering make use of recent advances in sparsity and rank minimization. However, existing techniques are computationally expensive and may result in degenerate solutions that degrade clustering performance in the case of insufficient data sampling. To partially solve these problems, and inspired by existing work on matrix factorization, this paper proposes fixed-rank representation (FRR) as a unified framework for unsupervised visual learning. FRR is able to reveal the structure of multiple subspaces in closed-form when the data is noiseless. Furthermore, we prove that under some suitable conditions, even with insufficient observations, FRR can still reveal the true subspace memberships. To achieve robustness to outliers and noise, a sparse regularizer is introduced into the FRR framework...

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Risheng Liu, Zhouchen Lin, Fernando De la Torre, Zhixun Su
• Keywords: Computer Vision, Cvpr 2012, Frr, Matrix Factorization, Numerical Solver