Regularized vector field learning with sparse approximation for mismatch removal
作者：
Highlights：
• A sparse approximation is presented for vector-valued regularized least-squares.
• We derive a bound for the sparse approximation.
• Based on it, we give a new robust vector field learning algorithm called SparseVFC.
• We apply SparseVFC to mismatch removal and it achieves state-of-the-art performance.
• The proposed method has linear time and space complexities in the scale of samples.
摘要
Highlights•A sparse approximation is presented for vector-valued regularized least-squares.•We derive a bound for the sparse approximation.•Based on it, we give a new robust vector field learning algorithm called SparseVFC.•We apply SparseVFC to mismatch removal and it achieves state-of-the-art performance.•The proposed method has linear time and space complexities in the scale of samples.
论文关键词：Vector field learning,Sparse approximation,Regularization,Reproducing kernel Hilbert space,Outlier,Mismatch removal
论文评审过程：Received 16 May 2012, Revised 28 February 2013, Accepted 21 May 2013, Available online 6 June 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.05.017
